Methods for radio wave based health monitoring that utilize data derived from amplitude and/or phase data

ABSTRACT

A method for monitoring a health parameter in a person is disclosed. The method involves transmitting radio waves below the skin surface of a person and across a range of stepped frequencies, receiving radio waves on a two-dimensional array of receive antennas, the received radio waves including a reflected portion of the transmitted radio waves across the range of stepped frequencies, generating data that corresponds to the received radio waves, wherein the data includes amplitude and phase data, deriving data from at least one of the amplitude and phase data, and determining a value that is indicative of a health parameter in the person in response to the derived data.

BACKGROUND

Diabetes is a medical disorder in which a person's blood glucose level,also known as blood sugar level, is elevated over an extended period oftime. If left untreated, diabetes can lead to severe medicalcomplications such as cardiovascular disease, kidney disease, stroke,foot ulcers, and eye damage. It has been estimated that the total costof diabetes in the U.S. in 2017 was $327 billion, American DiabetesAssociation, “Economic Costs of Diabetes in the U.S. in 2017,” publishedonline on Mar. 22, 2018.

Diabetes is typically caused by either the pancreas not producing enoughinsulin, referred to as “Type 1” diabetes, or because the cells of theperson do not properly respond to insulin that is produced, referred toas “Type 2” diabetes. Managing diabetes may involve monitoring aperson's blood glucose level and administering insulin when the person'sblood glucose level is too high to bring the blood glucose level down toa desired level. A person may need to measure their blood glucose levelup to ten times a day depending on many factors, including the severityof the diabetes and the person's medical history. Billions of dollarsare spent each year on equipment and supplies used to monitor bloodglucose levels.

SUMMARY

A method for monitoring a health parameter in a person is disclosed. Themethod involves transmitting radio waves below the skin surface of aperson and across a range of stepped frequencies, receiving radio waveson a two-dimensional array of receive antennas, the received radio wavesincluding a reflected portion of the transmitted radio waves across therange of stepped frequencies, generating data that corresponds to thereceived radio waves, wherein the data includes amplitude and phasedata, deriving data from at least one of the amplitude and phase data,and determining a value that is indicative of a health parameter in theperson in response to the derived data.

Other aspects in accordance with the invention will become apparent fromthe following detailed description, taken in conjunction with theaccompanying drawings, illustrated by way of example of the principlesof the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B are perspective views of a smartwatch.

FIG. 2A depicts a posterior view of a right hand with the typicalapproximate location of the cephalic vein and the basilic veinoverlaid/superimposed.

FIG. 2B depicts the location of a cross-section of the wrist from FIG.2A.

FIG. 2C depicts the cross-section of the wrist from the approximatelocation shown in FIG. 2B (as viewed in the direction from the elbow tothe hand).

FIG. 3 is a perspective view of human skin that includes a skin surface,hairs, and the epidermis and dermis layers of the skin.

FIG. 4A depicts a simplified version of the cross-section of FIG. 2C,which shows the skin, the radius and ulna bones, and the basilic vein.

FIG. 4B depicts the wrist cross-section of FIG. 4A in a case where asmartwatch is attached to the wrist.

FIG. 4C illustrates, in two dimensions, an example of the penetrationdepth (which corresponds to a 3D illumination space) of radio wavestransmitted from the sensor system of the smartwatch at a frequency of60 GHz and a transmission power of 15 dBm.

FIG. 4D illustrates, in two dimensions, an example of the penetrationdepth (which corresponds to a 3D illumination space) of radio wavestransmitted from the sensor system of the smartwatch at a frequency of122-126 GHz and transmit power of 15 dBm.

FIG. 5 depicts a functional block diagram of an embodiment of a sensorsystem that utilizes millimeter range radio waves to monitor a healthparameter such as the blood glucose level in a person.

FIG. 6 depicts an expanded view of an embodiment of portions of thesensor system of FIG. 5 , including elements of the RF front-end.

FIG. 7 depicts an embodiment of the IF/BB component shown in FIG. 6 .

FIG. 8A depicts an example embodiment of a plan view of an IC devicethat includes two TX antennas and four antennas 846 as well as some ofthe components from the RF front-end and the digital baseband (notshown) as described above with regard to FIGS. 5-7 .

FIG. 8B depicts an embodiment of a microstrip patch antenna that can beused for the TX and/or RX antennas of the IC device of FIG. 8A.

FIG. 8C depicts an example of the physical layout of circuit componentson a semiconductor substrate, such as the semiconductor substrate (die)depicted in FIG. 8A.

FIG. 8D depicts a packaged IC device similar to the packaged IC deviceshown in FIG. 8A superimposed over the semiconductor substrate shown inFIG. 8C.

FIG. 9 depicts an IC device similar to that of FIG. 8A overlaid on thehand/wrist that is described above with reference to FIG. 2A-2C.

FIG. 10 depicts an IC device similar to that of FIG. 8A overlaid on theback of the smartwatch.

FIG. 11 depicts a side view of a sensor system in a case in which thetwo TX antennas are configured parallel to veins such as the basilic andcephalic veins of a person wearing the smartwatch.

FIG. 12 depicts the same side view as shown in FIG. 11 in a case inwhich the two TX antennas are configured transverse to veins such as thebasilic and cephalic veins of a person wearing the smartwatch.

FIGS. 13A-13C depict frequency versus time graphs of impulse, chirp, andstepped frequency techniques for transmitting electromagnetic energy ina radar system.

FIG. 14 depicts a burst of electromagnetic energy using steppedfrequency transmission.

FIG. 15A depicts a graph of the transmission bandwidth, B, oftransmitted electromagnetic energy in the frequency range of 122-126GHz.

FIG. 15B depicts a graph of stepped frequency pulses that have arepetition interval, T, and a step size, Δf, of 62.5 MHz.

FIG. 16A depicts a frequency versus time graph of transmission pulses,with transmit (TX) interval and receive (RX) intervals identifiedrelative to the pulses.

FIG. 16B depicts an amplitude versus time graph of the transmissionwaveforms that corresponds to FIG. 16A.

FIG. 17 illustrates operations related to transmitting, receiving, andprocessing phases of the sensor system operation.

FIG. 18 depicts an expanded view of the anatomy of a wrist, similar tothat described above with reference to FIGS. 2A-4D, relative to RXantennas of a sensor system that is integrated into a wearable devicesuch as a smartwatch.

FIG. 19 illustrates an IC device similar to the IC device shown in FIG.8A relative to a vein and blood flowing through the vein.

FIG. 20 is an embodiment of a DSP that includes a Doppler effectcomponent, a beamforming component, and a ranging component.

FIG. 21 is a process flow diagram of a method for monitoring a healthparameter in a person.

FIG. 22A depicts a side view of the area around a person's ear with thetypical approximate locations of veins and arteries, including thesuperficial temporal artery, the superficial temporal vein, the anteriorauricular artery and vein, the posterior auricular artery, the occipitalartery, the external carotid artery, and the external jugular vein.

FIG. 22B depicts an embodiment of system in which at least elements ofan RF front-end are located separate from a housing.

FIG. 22C illustrates how a device, such as the device depicted in FIG.22B, may be worn near the ear of a person similar to how a conventionalhearing aid is worn.

FIG. 23 is a table of parameters related to stepped frequency scanningin a system such as the above-described system.

FIG. 24 is a table of parameters similar to the table of FIG. 23 inwhich examples are associated with each parameter for a given step in astepped frequency scanning operation in order to give some context tothe table.

FIG. 25 depicts an embodiment of the IC device from FIG. 8A in which theantenna polarization orientation is illustrated by the orientation ofthe transmit and receive antennas.

FIG. 26 is a table of raw data that is generated during steppedfrequency scanning.

FIG. 27 illustrates a system and process for machine learning that canbe used to identify and train a model that reflects correlations betweenraw data, derived data, and control data.

FIG. 28 is an example of a process flow diagram of a method forimplementing machine learning.

FIG. 29 is an example of a table of a raw data record generated duringstepped frequency scanning that is used to generate the training data.

FIGS. 30A-30D are tables of at least portions of raw data records thatare generated during a learning process that spans the time of t1-tn,where n corresponds to the number of time intervals, T, in the steppedfrequency scanning.

FIG. 31 illustrates a system for health parameter monitoring thatutilizes a sensor system similar to or the same as the sensor systemdescribed with reference to FIGS. 5-7 .

FIG. 32 is a process flow diagram of a method for monitoring a healthparameter in a person.

FIG. 33 is a process flow diagram of another method for monitoring ahealth parameter in a person.

FIG. 34 is a process flow diagram of a method for training a model foruse in monitoring a health parameter in a person.

Throughout the description, similar reference numbers may be used toidentify similar elements.

DETAILED DESCRIPTION

It will be readily understood that the components of the embodiments asgenerally described herein and illustrated in the appended figures couldbe arranged and designed in a wide variety of different configurations.Thus, the following more detailed description of various embodiments, asrepresented in the figures, is not intended to limit the scope of thepresent disclosure, but is merely representative of various embodiments.While the various aspects of the embodiments are presented in drawings,the drawings are not necessarily drawn to scale unless specificallyindicated.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the invention is, therefore, indicatedby the appended claims rather than by this detailed description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

Reference throughout this specification to features, advantages, orsimilar language does not imply that all of the features and advantagesthat may be realized with the present invention should be or are in anysingle embodiment of the invention. Rather, language referring to thefeatures and advantages is understood to mean that a specific feature,advantage, or characteristic described in connection with an embodimentis included in at least one embodiment of the present invention. Thus,discussions of the features and advantages, and similar language,throughout this specification may, but do not necessarily, refer to thesame embodiment.

Furthermore, the described features, advantages, and characteristics ofthe invention may be combined in any suitable manner in one or moreembodiments. One skilled in the relevant art will recognize, in light ofthe description herein, that the invention can be practiced without oneor more of the specific features or advantages of a particularembodiment. In other instances, additional features and advantages maybe recognized in certain embodiments that may not be present in allembodiments of the invention.

Reference throughout this specification to “one embodiment”, “anembodiment”, or similar language means that a particular feature,structure, or characteristic described in connection with the indicatedembodiment is included in at least one embodiment of the presentinvention. Thus, the phrases “in one embodiment”, “in an embodiment”,and similar language throughout this specification may, but do notnecessarily, all refer to the same embodiment.

Traditional blood glucose level monitoring is accomplished by pricking afinger to draw blood and measuring the blood glucose level with a bloodglucose meter, or “glucometer.” Continuous glucose monitoring can beaccomplished by applying a continuous glucose monitor (CGM) to an areaon the body such as the torso. The continuous glucose monitor utilizes aneedle that is continuously embedded through the skin to obtain accessto blood. Although blood glucose meters and continuous glucose monitorswork well to monitor blood glucose levels, both techniques are invasivein nature in that they require physical penetration of the skin by asharp object.

Various non-invasive techniques for monitoring blood glucose levels havebeen explored. Example techniques for monitoring blood glucose levelsinclude techniques based on infrared (IR) spectroscopy, near infrared(NIR) spectroscopy, mid infrared (MIR) spectroscopy, photoacousticspectroscopy, fluorescence spectroscopy, Raman spectroscopy, opticalcoherence tomography (OCT), and microwave sensing, Ruochong Zhang etal., “Noninvasive Electromagnetic Wave Sensing of Glucose,” Oct. 1,2018.

In the category of microwave sensing, millimeter range radio waves havebeen identified as useful for monitoring blood glucose levels. Anexample of using millimeter range radio waves to monitor blood glucoselevels is described by Peter H. Siegel et al., “Millimeter-WaveNon-Invasive Monitoring of Glucose in Anesthetized Rats,” 2014International Conference on Infrared, Millimeter, and Terahertz Waves,Tucson, Ariz., Sep. 14-19, 2014. Here, Siegel et al. describes using theKa band (27-40 GHz) to measure blood glucose levels through the ear of alab rat.

Another example of using millimeter range radio waves to monitor bloodglucose levels is described by George Shaker et al., “Non-InvasiveMonitoring of Glucose Level Changes Utilizing a mm-Wave Radar System,”International Journal of Mobile Human Computer Interaction, Volume 10,Issue 3, July-September 2018. Here, Shaker et al. utilizes a millimeterrange sensing system referred to as “Soli,” (see Jaime Lien et. al.,“Soli: Ubiquitous Gesture Sensing with Millimeter Wave Radar,” ACMTrans. Graph. 35, 4 Article 142, July 2016) to monitor blood glucoselevels. Shaker et al. utilizes radio waves in the 57-64 GHz frequencyrange to monitor blood glucose levels. Although the Soli sensor systemincludes transmit (TX) and receive (RX) antennas on the same integratedcircuit (IC) device (i.e., the same “chip”) and thus in the same plane,Shaker et al. concludes that for blood glucose monitoring, a radarsensing system configuration would ideally have its antennas placed onopposite sides of the sample under test to be able to effectivelymonitor blood glucose levels. When the transmit (TX) and receive (RX)antennas were on the same side of the sample under test, Shaker et al.was not able to find any discernible trend in the magnitude or phase ofthe sensor signals.

Another example of using millimeter range radio waves to monitor bloodglucose levels is described by Shimul Saha et al., “A Glucose SensingSystem Based on Transmission Measurements at Millimeter Waves usingMicro strip Patch Antennas,” Scientific Reports, published online Jul.31, 2017. Here, Saha et al. notes that millimeter wave spectroscopy inreflection mode has been used for non-invasive glucose sensing throughhuman skin, but concludes that signals from reflection mode detectionyield information that is insufficient for tracking the relevant changesin blood glucose levels. Saha et al. investigates radio waves in therange of 20-100 GHz for monitoring blood glucose levels and concludesthat an optimal sensing frequency is in the range of 40-80 GHz.

Although blood glucose level monitoring using millimeter range radiowaves has been shown to be technically feasible, implementation ofpractical monitoring methods and systems has yet to be realized. Forexample, a practical realization of a monitoring system may include amonitoring system that can be integrated into a wearable device, such asa smartwatch.

In accordance with an embodiment of the invention, methods and systemsfor monitoring the blood glucose level of a person using millimeterrange radio waves involve transmitting millimeter range radio wavesbelow the skin surface, receiving a reflected portion of the radio waveson multiple receive antennas, isolating a signal from a particularlocation in response to the received radio waves, and outputting asignal that corresponds to a blood glucose level in the person inresponse to the isolated signals. In an embodiment, beamforming is usedin the receive process to isolate radio waves that are reflected from aspecific location (e.g., onto a specific blood vessel) to provide ahigh-quality signal that corresponds to blood glucose levels in thespecific blood vessel. In another embodiment, Doppler effect processingcan be used to isolate radio waves that are reflected from a specificlocation (e.g., reflected from a specific blood vessel) to provide ahigh-quality signal that corresponds to blood glucose levels in thespecific blood vessel. Analog and/or digital signal processingtechniques can be used to implement beamforming and/or Doppler effectprocessing and digital signal processing of the received signals can beused to dynamically adjust (or “focus”) a received beam onto the desiredlocation. In still another embodiment, beamforming and Doppler effectprocessing can be used together to isolate radio waves that arereflected from a specific location (e.g., reflected from a specificblood vessel) to provide a high-quality signal that corresponds to bloodglucose levels in the specific blood vessel.

As described above, Siegal et al., Shaker et al., and Saha et al.,utilize radio waves in the range of about 27-80 GHz, commonly around 60GHz, to monitor blood glucose levels. Saha et al. discloses that afrequency of around 60 GHz is desirable for glucose detection usingelectromagnetic transmission data and notes that for increasingly higherfrequencies, the losses are prohibitively high for the signal-to-noiseratio (SNR) to exceed the noise level of a sensing instrument such as aVector Network Analyzer (VNA).

In contrast to conventional techniques, it has been discovered thatusing a higher frequency range, e.g., 122-126 GHz, to monitor bloodglucose levels can provide certain benefits that heretofore have notbeen recognized. For example, transmitting millimeter range radio wavesin the frequency range of 122-126 GHz results in a shallower penetrationdepth within a human body than radio waves in the frequency range around60 GHz for a similar transmission power. A shallower penetration depthcan reduce undesirable reflections (e.g., reflections off of bone anddense tissue such as tendons, ligaments, and muscle), which can reducethe signal processing burden and improve the quality of the desiredsignal that is generated from the location of a blood vessel.

Additionally, transmitting millimeter range radio waves in the frequencyrange of 122-126 GHz enables higher resolution sensing than radio wavesat around 60 GHz due to the shorter wavelengths, e.g., 2.46-2.38 mm for122-126 GHz radio waves versus 5 mm for 60 GHz radio waves. Higherresolution sensing allows a receive beam to be focused more precisely(e.g., through beamforming and/or Doppler effect processing) onto aparticular blood vessel, such as the basilic vein on the posterior ofthe wrist, which can also improve the quality of the desired signal.

Additionally, utilizing millimeter range radio waves in the frequencyrange of 122-126 GHz to monitor blood glucose levels enables the size ofthe corresponding transmit and receive antennas to be reduced incomparison to techniques that utilize radio waves in the frequency rangeof 20-80 GHz. For example, the size of antennas can be reduced by afactor of approximately two by using radio waves in the 122-126 GHzfrequency range instead of radio waves in the 60 GHz frequency range,which can enable a smaller form factor for the antennas and for theoverall sensor system. Additionally, the frequency range of 122-126 GHzis an unlicensed band of the industrial, scientific, and medical (ISM)radio bands as defined by the International Telecommunication Union(ITU) Radio Regulations. Thus, methods and systems for monitoring bloodglucose levels that are implemented using a frequency range of 122-126GHz do not require a license.

FIGS. 1A and 1B are perspective views of a smartwatch 100, which is adevice that provides various computing functionality beyond simplygiving the time. Smartwatches are well known in the field. Thesmartwatch includes a case 102 (also referred to as a “housing”) and astrap 104 (e.g., an attachment device) and the strap is typicallyattached to the case by lugs (not shown). FIG. 1A is a top perspectiveview of the smartwatch that shows a front face 106 of the case and acrown 108 and FIG. 1B is a back perspective view of the smartwatch thatshows a back plate of the case. FIG. 1B also includes a dashed lineblock 110 that represents a sensor system, such as a sensor system forhealth monitoring. The sensor system may be partially or fully embeddedwithin the case. In some embodiments, the sensor system may include asensor integrated circuit (IC) device or IC devices with transmit and/orreceive antennas integrated therewith. In some embodiments, the backplate of the case may have openings that allow radio waves to pass moreeasily to and from smartwatch. In some embodiments, the back plate ofthe case may have areas of differing materials that create channelsthrough which radio waves can pass more easily. For example, in anembodiment, the back plate of the case may be made primarily of metalwith openings in the metal at locations that correspond to sensorantennas that are filled with a material (e.g., plastic or glass) thatallows radio waves to pass to and from the smartwatch more easily thanthrough the metal case.

Although a smartwatch is described as one device in which a millimeterrange radio wave sensing system can be included, a millimeter rangeradio wave sensing system can be included in other sensing devices,including various types of wearable devices and/or devices that are notwearable but that are brought close to, or in contact with, the skin ofa person only when health monitoring is desired. For example, amillimeter range radio wave sensing system can be incorporated into asmartphone. In an embodiment, a millimeter range radio wave sensingsystem can be included in a health and fitness tracking device that isworn on the wrist and tracks, among other things, a person's movements.In another embodiment, a millimeter range radio wave sensing system canbe incorporated into a device such as dongle or cover (e.g., aprotective cover that is placed over a smartphone for protection) thatis paired (e.g., via a local data connection such as USB or BLUETOOTH)with a device such as a smartphone or smartwatch to implement healthmonitoring. For example, a dongle may include many of the componentsdescribed below with reference to FIG. 6 , while the paired device(e.g., the smartphone or smartwatch) includes a digital signalprocessing capability (e.g., through a Digital Signal Processor (DSP))and instruction processing capability (e.g., through a CentralProcessing Unit (CPU)). In another example, a millimeter range sensingsystem may be incorporated into a device that is attached to the ear. Inan embodiment, the sensing system could be attached to the lobe of theear or have an attachment element that wraps around the ear or wrapsaround a portion of the ear.

Wearable devices such as smartwatches and health and fitness trackersare often worn on the wrist similar to a traditional wristwatch. Inorder to monitor blood glucose levels using millimeter range radiowaves, it has been discovered that the anatomy of the wrist is animportant consideration. FIG. 2A depicts a posterior view of a righthand 212 with the typical approximate location of the cephalic vein 214and the basilic vein 216 overlaid/superimposed. FIG. 2B depicts thelocation of a cross-section of the wrist 218 from FIG. 2A and FIG. 2Cdepicts the cross-section of the wrist 218 from the approximate locationshown in FIG. 2B (as viewed in the direction from the elbow to thehand). In FIG. 2C, the cross-section is oriented on the page such thatthe posterior portion of the wrist is on the top and the anteriorportion of the wrist is on the bottom. The depth dimension of a wrist isidentified on the left side and typically ranges from 40-60 mm (based ona wrist circumference in the range of 140-190 mm). Anatomic features ofthe wrist shown in FIG. 2C include the abductor pollicis longus (APL),the extensor carpi radialis brevis (ECRB), the extensor carpi radialislongus (ECRL), the extensor carpi ulnaris (ECU), the extensor indicisproprius (EIP), the extensor pollicis brevis (EPB), the extensorpollicis longus (EPL), the flexor carpi ulnaris (FCU), the flexordigitorum superficialis (FDS), the flexor pollicis longus (FPL), thebasilic vein 216, the radius, the ulna, the radial artery, the mediannerve, the ulnar artery, and the ulnar nerve. FIG. 2C also depicts theapproximate location of the basilic vein in subcutaneous tissue 220below the skin 222. In some embodiments and as is disclosed below, thelocation of a blood vessel such as the basilic vein is of particularinterest to monitoring blood glucose levels using millimeter range radiowaves.

FIG. 3 is a perspective view of human skin 322 that includes a skinsurface 324, hairs 326, and the epidermis 328 and dermis 330 layers ofthe skin. The skin is located on top of subcutaneous tissue 320. In anexample, the thickness of human skin in the wrist area is around 1-4 mmand the thickness of the subcutaneous tissue may vary from 1-34 mm,although these thicknesses may vary based on many factors. As shown inFIG. 3 , very small blood vessels 332 (e.g., capillaries having adiameter in the range of approximately 5-10 microns) are located aroundthe interface between the dermis and the subcutaneous tissue whileveins, such as the cephalic and basilic veins, are located in thesubcutaneous tissue just below the skin. For example, the cephalic andbasilic veins may have a diameter in the range of 1-4 mm and may beapproximately 2-10 mm below the surface of the skin, although thesediameters and depths may vary based on many factors. FIG. 3 depicts anexample location of the basilic vein 316 in the area of the wrist.

FIG. 4A depicts a simplified version of the cross-section of FIG. 2C,which shows the skin 422, the radius and ulna bones 434 and 436, and thebasilic vein 416. FIG. 4B depicts the wrist cross-section of FIG. 4A ina case where a smartwatch 400, such as the smartwatch shown in FIGS. 1Aand 1B, is attached to the wrist. FIG. 4B illustrates an example of thelocation of the smartwatch relative to the wrist and in particularrelative to the basilic vein of the wrist. In the example of FIG. 4B,dashed line block 410 represents the approximate location of a sensorsystem and corresponds to the dashed line block 110 shown in FIG. 1B.The location of the smartwatch relative to the anatomy of the wrist,including the bones and a vein such as the basilic vein, is an importantconsideration in implementing blood glucose monitoring using millimeterrange radio waves.

The magnitude of the reflected and received radio waves is a function ofthe power of the transmitted radio waves. With regard to the anatomy ofthe human body, it has been realized that radio waves transmitted ataround 60 GHz at a particular transmission power level (e.g., 15 dBm)penetrate deeper (and thus illuminate a larger 3D space) into the humanbody than radio waves transmitted at 122-126 GHz at the sametransmission power level (e.g., 15 dBm). FIG. 4C illustrates, in twodimensions, an example of the penetration depth (which corresponds to a3D illumination space) of radio waves 438 transmitted from the sensorsystem of the smartwatch at a frequency of 60 GHz and a transmissionpower of 15 dBm. FIG. 4D illustrates, in two dimensions, an example ofthe penetration depth (which corresponds to a 3D illumination space) ofradio waves 440 transmitted from the sensor system of the smartwatch ata frequency of 122-126 GHz and transmit power of 15 dBm, which is thesame transmission power as used in the example of FIG. 4C. Asillustrated by FIGS. 4C and 4D, for equivalent transmission powers(e.g., 15 dBm), radio waves 438 transmitted at 60 GHz penetrate deeperinto the wrist (and thus have a corresponding larger illumination space)than radio waves 440 that are transmitted at 122-126 GHz. The deeperpenetration depth of the 60 GHz radio waves results in more radio wavesbeing reflected from anatomical features within the wrist. For example,a large quantity of radio waves will be reflected from the radius andulna bones 434 and 436 in the wrist as well as from dense tissue such astendons and ligaments that are located between the skin and the bones atthe posterior of the wrist, see FIG. 2C, which shows tendons andligaments that are located between the skin and the bones at theposterior of the wrist. Likewise the shallower penetration of the122-126 GHz radio waves results in fewer radio waves being reflectedfrom undesired anatomical features within the wrist (e.g., anatomicalfeatures other than the targeted blood vessel or vein). For example, amuch smaller or negligible magnitude of radio waves will be reflectedfrom the radius and ulna bones in the wrist as well as from dense tissuesuch as tendons and ligaments that are located between the skin and thebones at the posterior of the wrist.

It has been realized that the penetration depth (and corresponding 3Dillumination space), is an important factor in the complexity of thesignal processing that is performed to obtain an identifiable signalthat corresponds to the blood glucose level in the wrist (e.g., in thebasilic vein of the wrist). In order to accurately measure the bloodglucose level in a vein such as the basilic vein, it is desirable toisolate reflections from the area of the vein from all of the otherreflections that are detected (e.g., from reflections from the radiusand ulna bones in the wrist as well as from dense tissue such as tendonsand ligaments that are located between the skin and the bones at theposterior of the wrist). In an embodiment, radio waves are transmittedat an initial power such that the power of the radio waves hasdiminished by approximately one-half (e.g., ±10%) at a depth of 6 mmbelow the skin surface. Reflections can be isolated using varioustechniques including signal processing techniques that are used forbeamforming, Doppler effect, and/or leakage mitigation. The largerquantity of reflections in the 60 GHz case will likely need moreintensive signal processing to remove signals that correspond tounwanted reflections in order to obtain a signal of sufficient qualityto monitor a blood parameter such as the blood glucose level in aperson.

FIG. 5 depicts a functional block diagram of an embodiment of a sensorsystem 510 that utilizes millimeter range radio waves to monitor ahealth parameter such as the blood glucose level in a person. The sensorsystem includes transmit (TX) antennas 544, receive (RX) antennas 546,an RF front-end 548, a digital baseband system 550, and a CPU 552. Thecomponents of the sensor system may be integrated together in variousways. For example, some combination of components may be fabricated onthe same semiconductor substrate and/or included in the same packaged ICdevice or a combination of packaged IC devices. As described above, inan embodiment, the sensor system is designed to transmit and receiveradio waves in the range of 122-126 GHz.

In the embodiment of FIG. 5 , the sensor system 510 includes two TXantennas 544 and four RX antennas 546. Although two TX and four RXantennas are used, there could be another number of antennas, e.g., oneor more TX antennas and two or more RX antennas. In an embodiment, theantennas are configured to transmit and receive millimeter range radiowaves. For example, the antennas are configured to transmit and receiveradio waves in the 122-126 GHz frequency range, e.g., wavelengths in therange of 2.46-2.38 mm.

In the embodiment of FIG. 5 , the RF front-end 548 includes a transmit(TX) component 554, a receive (RX) component 556, a frequencysynthesizer 558, and an analogue processing component 560. The transmitcomponent may include elements such as power amplifiers and mixers. Thereceive component may include elements such as low noise amplifiers(LNAs), variable gain amplifiers (VGAs), and mixers. The frequencysynthesizer includes elements to generate electrical signals atfrequencies that are used by the transmit and receive components. In anembodiment the frequency synthesizer may include elements such as acrystal oscillator, a phase-locked loop (PLL), a frequency doubler,and/or a combination thereof. The analogue processing component mayinclude elements such as mixers and filters, e.g., low pass filters(LPFs). In an embodiment, components of the RF front-end are implementedin hardware as electronic circuits that are fabricated on the samesemiconductor substrate.

The digital baseband system 550 includes an analog-to-digital converter(ADC) 562, a digital signal processor (DSP) 564, and a microcontrollerunit (MCU) 566. Although the digital baseband system is shown asincluding certain elements, the digital baseband system may include someother configuration, including some other combination of elements. Thedigital baseband system is connected to the CPU 552 via a bus.

FIG. 6 depicts an expanded view of an embodiment of portions of thesensor system 510 of FIG. 5 , including elements of the RF front-end. Inthe embodiment of FIG. 6 , the elements include a crystal oscillator670, a phase locked loop (PLL) 672, a bandpass filter (BPF) 674, a mixer676, power amplifiers (PAs) 678, TX antennas 644, a frequencysynthesizer 680, a frequency doubler 682, a frequency divider 684, amixer 686, an RX antenna 646, a low noise amplifier (LNA) 688, a mixer690, a mixer 692, and an Intermediate Frequency/Baseband (IF/BB)component 694. As illustrated in FIG. 6 , the group of receivecomponents identified within and dashed box 696 is repeated four times,e.g., once for each of four distinct RX antennas.

Operation of the system shown in FIG. 6 is described with reference to atransmit operation and with reference to a receive operation. Thedescription of a transmit operation generally corresponds to aleft-to-right progression in FIG. 6 and description of a receiveoperation generally corresponds to a right-to-left progression in FIG. 6. With regard to the transmit operation, the crystal oscillator 670generates an analog signal at a frequency of 10 MHz. The 10 MHz signalis provided to the PLL 672, to the frequency synthesizer 680, and to thefrequency divider 684. The PLL uses the 10 MHz signal to generate ananalog signal that is in the 2-6 GHz frequency range. The 2-6 GHz signalis provided to the BPF 674, which filters the input signal and passes asignal in the 2-6 GHz range to the mixer 676. The 2-6 GHz signal is alsoprovided to the mixer 686.

Dropping down in FIG. 6 , the 10 MHz signal is used by the frequencysynthesizer 680 to produce a 15 GHz signal. The 15 GHz signal is used bythe frequency doubler 682 to generate a signal at 120 GHz. In anembodiment, the frequency doubler includes a series of three frequencydoublers that each double the frequency, e.g., from 15 GHz to 30 GHz,and then from 30 GHz to 60 GHz, and then from 60 GHz to 120 GHz. The 120GHz signal and the 2-6 GHz signal are provided to the mixer 676, whichmixes the two signals to generate a signal at 122-126 GHz depending onthe frequency of the 2-6 GHz signal. The 122-126 GHz signal output fromthe mixer 676 is provided to the power amplifiers 678, and RF signals inthe 122-126 GHz range are output from the TX antennas 644. In anembodiment, the 122-126 GHz signals are output at 15 dBm (decibels (dB)with reference to 1 milliwatt (mW)). In an embodiment and as describedbelow, the PLL is controlled to generate discrete frequency pulsesbetween 2-6 GHz that are used for stepped frequency transmission.

The 10 MHz signal from the crystal oscillator 670 is also provided tothe frequency divider 684, which divides the frequency down, e.g., from10 MHz to 2.5 MHz via, for example, two divide by two operations, andprovides an output signal at 2.5 MHz to the mixer 686. The mixer 686also receives the 2-6 GHz signal from the BPF 674 and provides a signalat 2-6 GHz+2.5 MHz to the mixer 692 for receive signal processing.

With reference to a receive operation, electromagnetic (EM) energy isreceived at the RX antenna 646 and converted to electrical signals,e.g., voltage and current. For example, electromagnetic energy in the122-126 GHz frequency band is converted to an electrical signal thatcorresponds in frequency (e.g., GHz), magnitude (e.g., power in dBm),and phase to the electromagnetic energy that is received at the RXantenna. The electrical signal is provided to the LNA 688. In anembodiment, the LNA amplifies signals in the 122-126 GHz frequency rangeand outputs an amplified 122-126 GHz signal. The amplified 122-126 GHzsignal is provided to the mixer 690, which mixes the 120 GHz signal fromthe frequency doubler 682 with the received 122-126 GHz signal togenerate a 2-6 GHz signal that corresponds to the electromagnetic energythat was received at the RX antenna. The 2-6 GHz signal is then mixedwith the 2-6 GHz+2.5 MHz signal at mixer 692 to generate a 2.5 MHzsignal that corresponds to the electromagnetic energy that was receivedat the RX antenna. For example, when a 122 GHz signal is beingtransmitted from the TX antennas and received at the RX antenna, themixer 692 receives a 2 GHz signal that corresponds to theelectromagnetic energy that was received at the antenna and a 2 GHz+2.5MHz signal from the mixer 686. The mixer 692 mixes the 2 GHz signal thatcorresponds to the electromagnetic energy that was received at the RXantenna with the 2 GHz+2.5 MHz signal from the mixer 686 to generate a2.5 MHz signal that corresponds to the electromagnetic energy that wasreceived at the RX antenna. The 2.5 MHz signal that corresponds to theelectromagnetic energy that was received at the RX antenna is providedto the IF/BB component 694 for analog-to-digital conversion. Theabove-described receive process can be implemented in parallel on eachof the four receive paths 696. As is described below, the systemdescribed with reference to FIG. 6 can be used to generate variousdiscrete frequencies that can be used to implement, for example, steppedfrequency radar detection. As described above, multiple mixingoperations are performed to implement a sensor system at such a highfrequency, e.g., in the 122-126 GHz range. The multiple mixers andcorresponding mixing operations implement a “compound mixing”architecture that enables use of such high frequencies.

FIG. 7 depicts an embodiment of the IF/BB component 794 shown in FIG. 6. The IF/BB component of FIG. 7 includes similar signal paths 702 foreach of the four receive paths/RX antennas and each signal path includesa low pass filter (LPF) 704, an analog-to-digital converter (ADC) 762, amixer 706, and a decimation filter 708. The operation of receive path 1,RX1, is described.

As described above with reference to FIG. 6 , the 2.5 MHz signal frommixer 692 (FIG. 6 ) is provided to the IF/BB component 694/794, inparticular, to the LPF 704 of the IF/BB component 794. In an embodiment,the LPF filters the 2.5 MHz signal to remove the negative frequencyspectrum and noise outside of the desired bandwidth. After passingthrough the LPF, the 2.5 MHz signal is provided to the ADC 762, whichconverts the 2.5 MHz signal (e.g., IF signal) to digital data at asampling rate of 10 MHz (e.g., as 12-16 bits of “real” data). The mixer706 multiplies the digital data with a complex vector to generate adigital signal (e.g., 12-16 bits of “complex” data), which is alsosampled at 10 MHz. Although the signal is sampled at 10 MHz, othersampling rates are possible, e.g., 20 MHz. The digital data sampled at10 MHz is provided to the decimation filter, which is used to reduce theamount of data by selectively discarding a portion of the sampled data.For example, the decimation filter reduces the amount of data byreducing the sampling rate and getting rid of a certain percentage ofthe samples, such that fewer samples are retained. The reduction insample retention can be represented by a decimation factor, M, and maybe, for example, about 10 or 100 depending on the application, where Mequals the input sample rate divided by the output sample rate.

The output of the decimation filter 708 is digital data that isrepresentative of the electromagnetic energy that was received at thecorresponding RX antenna. In an embodiment, samples are output from theIF/BB component 794 at rate of 1 MHz (using a decimation factor of 10)or at a rate of 100 kHz (using a decimation factor of 100). The digitaldata is provided to a DSP and/or CPU 764 via a bus 710 for furtherprocessing. For example, the digital data is processed to isolate asignal from a particular location, e.g., to isolate signals thatcorrespond to electromagnetic energy that was reflected by the blood ina vein of the person. In an embodiment, signal processing techniques areapplied to implement beamforming, Doppler effect processing, and/orleakage mitigation to isolate a desired signal from other undesiredsignals.

In conventional RF systems, the analog-to-digital conversion processinvolves a high direct current (DC), such that the I (“real”) and Q(“complex”) components of the RF signal at DC are lost at the ADC. Usingthe system as described above with reference to FIGS. 5-7 , theintermediate IF is not baseband, so I and Q can be obtained afteranalog-to-digital conversion and digital mixing as shown in FIG. 7 .

In an embodiment, digital signal processing of the received signals mayinvolve implementing Kalman filters to smooth out noisy data. In anotherembodiment, digital signal processing of the received signals mayinvolve combining receive chains digitally. Other digital signalprocessing may be used to implement beamforming, Doppler effectprocessing, and ranging. Digital signal processing may be implemented ina DSP and/or in a CPU.

In an embodiment, certain components of the sensor system are integratedonto a single semiconductor substrate and/or onto a single packaged ICdevice (e.g., a packaged IC device that includes multiple differentsemiconductor substrates (e.g., different die) and antennas). Forexample, elements such as the components of the RF front-end 548, and/orcomponents of the digital baseband system 550 (FIGS. 5-7 ) areintegrated onto the same semiconductor substrate (e.g., the same die).In an embodiment, components of the sensor system are integrated onto asingle semiconductor substrate that is approximately 5 mm×5 mm. In anembodiment, the TX antennas and RX antennas are attached to an outersurface of the semiconductor substrate and/or to an outer surface of anIC package and electrically connected to the circuits integrated intothe semiconductor substrate. In an embodiment, the TX and RX antennasare attached to the outer surface of the IC package such that the TX andRX antenna attachments points are very close to the correspondingtransmit and receive circuits such as the PAs and LNAs. In anembodiment, the semiconductor substrate and the packaged IC deviceincludes outputs for outputting electrical signals to another componentssuch as a DSP, a CPU, and or a bus. In some embodiments, the packaged ICdevice may include the DSP and/or CPU or the packaged IC device mayinclude some DSP and/or CPU functionality.

FIG. 8A depicts an example embodiment of a plan view of an IC device 820that includes two TX antennas 844 and four RX antennas 846 as well assome of the components from the RF front-end and the digital baseband(not shown) as described above with regard to FIGS. 5-7 . In FIG. 8A,the outer footprint of the IC device represents a packaged IC device 822and the inner footprint (as represented by the dashed box 824)represents a semiconductor substrate that includes circuits that arefabricated into the semiconductor substrate to conduct and processelectrical signals that are transmitted by the TX antennas and/orreceived by the RX antennas. In the embodiment of FIG. 8A, the packagedIC device has dimensions of 5 mm×5 mm (e.g., referred to as the device“footprint”) and the semiconductor substrate has a footprint that isslightly smaller than the footprint of the packaged IC device, e.g., thesemiconductor substrate has dimensions of approximately 0.1-1 mm lessthan the packaged IC device on each side. Although not shown, in anexample embodiment, the packaged IC device has a thickness ofapproximately 0.3-2 mm and the semiconductor substrate has a thicknessin the range of about 0.1-0.7 mm. In an embodiment, the TX and RXantennas are designed for millimeter range radio waves, for example,radio waves of 122-126 GHz have wavelengths in the range of 2.46 to 2.38mm. In FIG. 8A, the TX and RX antennas are depicted as square boxes ofapproximately 1 mm×1 mm and the antennas are all attached on the sameplanar surface of the IC device package. For example, the antennas areattached on the top surface of the IC package (e.g., on top of a ceramicpackage material) directly above the semiconductor substrate withconductive vias that electrically connect a conductive pad of thesemiconductor substrate to a transmission line of the antenna. Althoughthe TX and RX antennas may not be square, the boxes correspond to anapproximate footprint of the TX and RX antennas. In an embodiment, theantennas are microstrip patch antennas and the dimensions of theantennas are a function of the wavelength of the radio waves. Othertypes of antennas such as dipole antennas are also possible. FIG. 8Bdepicts an embodiment of a microstrip patch antenna 830 that can be usedfor the TX and/or RX antennas 844 and 846 of the IC device of FIG. 8A.As shown in FIG. 8B, the microstrip patch antenna has a patch portion832 (with dimensions length (L)×width (W)) and a microstrip transmissionline 834. In some embodiments, microstrip patch antennas have length andwidth dimensions of one-half the wavelength of the target radio waves.Thus, microstrip patch antennas designed for radio waves of 122-126 GHz(e.g., wavelengths in the range of 2.46 to 2.38 mm), the patch antennasmay have length and width dimensions of around 1.23-1.19 mm, but no morethan 1.3 mm. It is noted that because antenna size is a function ofwavelength, the footprint of the antennas shown in FIGS. 8A and 8B canbe made to be around one-half the size of antennas designed for radiowaves around 60 GHz (e.g., wavelength of approximately 5 mm).Additionally, the small antenna size of the antennas shown in FIGS. 8Aand 8B makes it advantageous to attach all six of the antennas to thetop surface of the package of the IC device within the footprint of thesemiconductor substrate, which makes the packaged IC device more compactthan known devices such as the “Soli” device. That is, attaching all ofthe TX and RX antennas within the footprint of the semiconductorsubstrate (or mostly within the footprint of the semiconductorsubstrate, e.g., greater than 90% within the footprint).

In an embodiment, the RX antennas form a phased antenna array and forthe application of health monitoring it is desirable to have as muchspatial separation as possible between the RX antennas to improveoverall signal quality by obtaining unique signals from each RX antenna.For example, spatial separation of the RX antennas enables improveddepth discrimination to isolate signals that correspond to reflectionsfrom blood in a vein from reflections from other anatomical features.Thus, as shown in FIG. 8A, the RX antennas 846 are located at thecorners of the rectangular shaped IC device. For example, the RXantennas are located flush with the corners of the semiconductorsubstrate 824 and/or flush with the corners of the IC device package orwithin less than about 0.5 mm from the corners of the semiconductorsubstrate 824 and/or from the corners of the IC device package. Althoughthe IC device shown in FIG. 8A has dimensions of 5 mm×5 mm, IC deviceshaving smaller (e.g., approximately 3 mm×3 mm) or larger dimensions arepossible. In an embodiment, the IC device has dimensions of no more than7 mm×7 mm.

In the embodiment of FIG. 8A, the TX antennas 844 are located onopposite sides of the IC chip approximately in the middle between thetwo RX antennas 846 that are on the same side. As shown in FIG. 8A, theTX antenna on the left side of the IC device is vertically aligned withthe two RX antennas on the left side of the IC device and the TX antennaon the right side of the IC device is vertically aligned with the two RXantennas on the right side of the IC device. Although one arrangement ofthe TX and RX antennas is shown in FIG. 8A, other arrangements arepossible.

At extremely high frequencies (e.g., 30-300 GHz) conductor losses can bevery significant. Additionally, conductor losses at extremely highfrequencies are known to be frequency-dependent, with higher frequenciesexhibiting higher conductor losses. In many health monitoringapplications, power, such as battery power, is a limited resource thatmust be conserved. Additionally, for reasons as described above such aslimiting undesired reflections, low power transmissions may be desirablefor health monitoring reasons. Because of the low power environment,conductor losses can severely impact performance of the sensor system.For example, significant conductor losses can occur between the antennasand the conductive pads of the semiconductor substrate, or “die,” andbetween the conductive pads and the transmit/receive components in thedie, e.g., the channel-specific circuits such as amplifiers, filters,mixers, etc. In order to reduce the impact of conductor losses in thesensor system, it is important to locate the antennas as close to thechannel-specific transmit/receive components of the die as possible. Inan embodiment, the transmit and receive components are strategicallyfabricated on the semiconductor substrate in locations that correspondto the desired locations of the antennas. Thus, when the TX and RXantennas are physically and electrically attached to the IC device, theTX and RX antennas are as close as possible to the transmit and receivecomponents on the die, e.g., collocated such that a portion of thechannel specific transmit/receive component overlaps from a plan viewperspective a portion of the respective TX/RX antenna. FIG. 8C depictsan example of the physical layout of circuit components on asemiconductor substrate, such as the semiconductor substrate (die)depicted in FIG. 8A. In the embodiment of FIG. 8C, the die 824 includestwo TX components 854, four RX components 856, shared circuits 860, andan input/output interface (I/O) 862. In the example of FIG. 8C, each TXcomponent includes channel-specific circuits (not shown) such asamplifiers, each RX component includes channel-specific circuits (notshown) such as mixers, filters, and LNAs, and the shared circuitsinclude, for example, a voltage control oscillator (VCO), a localoscillator (LO), frequency synthesizers, PLLs, BPFs, divider(s), mixers,ADCs, buffers, digital logic, a DSP, CPU, or some combination thereofthat may be utilized in conjunction with the channel-specific TX and RXcomponents. As shown in FIG. 8C, the transmit and receive components 854and 856 each include an interface 864 (such as a conductive pad) thatprovides an electrical interface between the circuits on the die and acorresponding antenna. FIG. 8D depicts a packaged IC device 822 similarto the packaged IC device shown in FIG. 8A superimposed over thesemiconductor substrate 824 shown in FIG. 8C. FIG. 8D illustrates thelocations of the TX and RX antennas 844 and 846 relative to the transmitand receive components 854 and 856 of the die (from a plan viewperspective). As illustrated in FIG. 8D, the TX and RX antennas 844 and846 are located directly over the interfaces 864 of the correspondingtransmit and receive components 854 and 856. In an embodiment in whichthe antennas are attached to a top surface of the package (which may beless than 0.5 mm thick), the antennas can be connected to the interfaceof the respective transmit/receive components by a distance that is afraction of a millimeter. In an embodiment, a via that is perpendicularto the plane of the die connects the interface of the transmit/receivecomponent to a transmission line of the antenna. More than one via maybe used when the antenna has more than one transmission line. Such acollocated configuration enables the desired distribution of the TX andRX antennas to be maintained while effectively managing conductor lossesin the system. Such a close proximity between antennas andchannel-specific circuits of the die is extremely important atfrequencies in the 122-126 GHz range and provides an improvement oversensor systems that include conductive traces of multiple millimetersbetween the antennas and the die.

Although the example of FIGS. 8A-8D shows the antennas within thefootprint of the packaged IC device 822, in some other embodiments, theantennas may extend outside the footprint of the die and/or the packagedIC device while still being collocated with the correspondingtransmit/receive components on the die. For example, the antennas may bedipole antennas that have portions of the antennas that extend outsidethe footprint of the die and/or the packaged IC device.

It has been realized that for the application of monitoring a healthparameter such as the blood glucose level in the blood of a person, itis important that the TX antennas are able to illuminate at least onevein near the skin of the person. In order for a TX antenna toilluminate at least one vein near the skin of the person, it isdesirable for at least one of the antennas to be spatially close to avein. Because of variations in the locations of veins relative to thelocation of the monitoring system (e.g., a smartwatch), it has beenfound that a transverse configuration of the TX antennas relative to theexpected location of a vein or veins provides desirable conditions formonitoring a health parameter such as the blood glucose level in theblood of a person. When the wearable device is worn on a portion of alimb such as the wrist, the TX antennas are distributed in a transverseconfiguration relative to the limb and relative to the expected locationof a vein or veins that will be illuminated by the TX antennas.

FIG. 9 depicts an IC device 922 similar to that of FIG. 8A overlaid onthe hand/wrist 912 that is described above with reference to FIG. 2A-2C.The IC device is oriented with regard to the basilic and cephalic veins914 and 916 such that the two TX antennas 944 are configured transverseto the basilic and cephalic veins. That is, the two TX antennas aredistributed transversely relative to the orientation (e.g., the lineardirection) of the vessel or vessels that will be monitored, such as thebasilic and cephalic veins. For example, in a transverse configuration,a straight line that passes through the two TX antennas would betransverse to the vessel or vessels that will be monitored, such as thebasilic and cephalic veins. In an embodiment in which the wearabledevice is worn on the wrist, the transverse configuration of the TXantennas is such that a line passing through both of the TX antennas isapproximately orthogonal to the wrist and approximately orthogonal tothe orientation of the vessel or vessels that will be monitored, such asthe basilic and cephalic veins. For example, a line passing through bothof the TX antennas and the orientation of the vessel or vessels thatwill be monitored, such as the basilic and cephalic veins, may bewithout about 20 degrees from orthogonal.

FIG. 10 depicts an IC device 1022 similar to that of FIG. 8A overlaid onthe back of the smartwatch 1000 described above with reference to FIGS.1A and 1B. As shown in FIGS. 9 and 10 , the two TX antennas areconfigured such that when the smartwatch is worn on the wrist, the twoTX antennas are transverse to veins such as the basilic and cephalicveins that run parallel to the length of the arm and wrist.

FIGS. 11 and 12 are provided to illustrate the expanded illuminationvolume that can be achieved by a sensor system 1010 that includes atransverse TX antenna configuration. FIG. 11 depicts a side view of asensor system in a case in which the two TX antennas 1044 are configuredparallel to veins such as the basilic and cephalic veins of a personwearing the smartwatch 1000. In the view shown in FIG. 11 , the two TXantennas are in-line with each other such that only one of the two TXantennas is visible from the side view. When the TX antennas transmitmillimeter range radio waves, the electromagnetic energy may have atwo-dimensional (2D) illumination pattern as illustrated by dashed line1020. Given the two-dimensional pattern as illustrated in FIG. 11 , thetwo TX antennas illuminate an area that has a maximum width in thetransverse direction (transverse to veins that run parallel to thelength of the arm and wrist and referred to herein as the transversewidth) identified by arrow 1022. Although the illumination pattern isdescribed and illustrated in two dimensions (2D), it should beunderstood that illumination actually covers a 3D space or volume.

FIG. 12 depicts the same side view as shown in FIG. 11 in a case inwhich the two TX antennas 1044 are configured transverse to veins suchas the basilic and cephalic veins of a person wearing the smartwatch1000. In the view shown in FIG. 12 , the two TX antennas are spatiallyseparated from each other such that both of the TX antennas are visiblefrom the side view. When the TX antennas transmit millimeter range radiowaves, the electromagnetic energy may have a 2D illumination pattern asillustrated by dashed lines 1024. Given the 2D elimination patterns ofthe two TX antennas, the two TX antennas combine to illuminate an areathat has a width in the transverse direction (transverse width)identified by arrow 1026, which is wider than the transverse width forthe TX antenna configuration shown in FIG. 11 (e.g., almost twice aswide). A wider illumination area improves the coverage area for thesensor system 1010 and increases the likelihood that the sensor systemwill illuminate a vein in the person wearing the smartwatch. Anincreased likelihood that a vein is illuminated can provide morereliable feedback from the feature of interest (e.g., blood in the vein)and thus more reliable monitoring results. Additionally, a widerillumination area can increase the power of the radio waves thatilluminate a vein, resulting in an increase in the power of theelectromagnetic energy that is reflected from the vein, which canimprove the quality of the received signals.

It has been established that the amount of glucose in the blood (bloodglucose level) affects the reflectivity of millimeter range radio waves.However, when millimeter range radio waves are applied to the human body(e.g., at or near the skin surface), electromagnetic energy is reflectedfrom many objects including the skin itself, fibrous tissue such asmuscle and tendons, and bones. In order to effectively monitor a healthparameter such as the blood glucose level of a person, electricalsignals that correspond to electromagnetic energy that is reflected fromblood (e.g., from the blood in a vein) should be isolated fromelectrical signals that correspond to electromagnetic energy that isreflected from other objects such as the skin itself, fibrous tissue,and bone, as well as from electrical signals that correspond toelectromagnetic energy that is emitted directly from the TX antennas(referred to herein as electromagnetic energy leakage or simply as“leakage”) and received by an antenna without passing through the skinof the person.

Various techniques that can be implemented alone or in combination toisolate electrical signals that correspond to reflections from bloodfrom other electrical signals that correspond to other reflections (suchas reflections from bone and/or fibrous tissue such as muscle andtendons) and/or signals that correspond to leakage are described below.Such techniques relate to and/or involve, for example, transmissioncharacteristics, beamforming, Doppler effect processing, leakagemitigation, and antenna design.

As is known in the field, radar detection involves transmittingelectromagnetic energy and receiving reflected portions of thetransmitted electromagnetic energy. Techniques for transmittingelectromagnetic energy in radar systems include impulse, chirp, andstepped frequency techniques.

FIGS. 13A-13C depict frequency versus time graphs of impulse, chirp, andstepped frequency techniques for transmitting electromagnetic energy ina radar system. FIG. 13A depicts a radar transmission technique thatinvolves transmitting pulses of electromagnetic energy at the samefrequency for each pulse, referred to as “impulse” transmission. In theexample of FIG. 13A, each pulse is at frequency, f₁, and lasts for aconstant interval of approximately 2 ns. The pulses are each separatedby approximately 2 ns.

FIG. 13B depicts a radar transmission technique that involvestransmitting pulses of electromagnetic energy at an increasing frequencyfor each interval, referred to herein as “chirp” transmission. In theexample of FIG. 13B, each chirp increases in frequency from frequency f₀to f₁ over an interval of 2 ns and each chirp is separated by 2 ns. Inother embodiments, the chirps may be separated by very short intervals(e.g., a fraction of a nanosecond) or no interval.

FIG. 13C depicts a radar transmission technique that involvestransmitting pulses of electromagnetic energy at the same frequencyduring a particular pulse but at an increased frequency frompulse-to-pulse, referred to herein as a “stepped frequency” transmissionor a stepped frequency pattern. In the example of FIG. 13C, each pulsehas a constant frequency over the interval of the pulse (e.g., over 2ns), but the frequency increases by an increment of Δf frompulse-to-pulse. For example, the frequency of the first pulse is f₀, thefrequency of the second pulse is f₀+Δf, the frequency of the third pulseis f₀+2Δf, and the frequency of the fourth pulse is f₀+3Δf, and so on.

In an embodiment, the sensor system described herein is operated usingstepped frequency transmission. Operation of the sensor system usingstepped frequency transmission is described in more detail below. FIG.14 depicts a burst of electromagnetic energy using stepped frequencytransmission. The frequency of the pulses in the burst can be expressedas:f _(n) =f ₀ +nΔfwhere f₀=starting carrier frequency, Δf=step size, τ=pulse length(active, per frequency), T=repetition interval, n=1, . . . N, each burstconsists of N pulses (frequencies) and a coherent processing interval(CPI)=N·T=1 full burst.

Using stepped frequency transmission enables relatively high rangeresolution. High range resolution can be advantageous when trying tomonitor a health parameter such as the blood glucose level in a veinthat may, for example, have a diameter in the range of 1-4 mm. Forexample, in order to effectively isolate a signal that corresponds toreflections of electromagnetic energy from the blood in a 1-4 mmdiameter vein, it is desirable to have a high range resolution, which isprovided by the 122-126 GHz frequency range.

Using stepped frequency transmission, range resolution can be expressedas:ΔR=c/2Bwherein c=speed of light, B=effective bandwidth. The range resolutioncan then be expressed as:ΔR=c/2N·Δfwherein B=N·Δf. Thus, range resolution does not depend on instantaneousbandwidth and the range resolution can be increased arbitrarily byincreasing N·Δf.

In an embodiment, the electromagnetic energy is transmitted from the TXantennas in the frequency range of approximately 122-126 GHz, whichcorresponds to a total bandwidth of approximately 4 GHz, e.g., B=4 GHz.FIG. 15A depicts a graph of the transmission bandwidth, B, oftransmitted electromagnetic energy in the frequency range of 122-126GHz. Within a 4 GHz bandwidth, from 122-126 GHz, discrete frequencypulses can be transmitted. For example, in an embodiment, the number ofdiscrete frequencies that can be transmitted ranges from, for example,64-256 discrete frequencies. In a case with 64 discrete frequency pulsesand a repetition interval, T, over 4 GHz of bandwidth, the step size,Δf, is 62.5 MHz (e.g., 4 GHz of bandwidth divided by 64=62.5 MHz) and ina case with 256 discrete frequency pulses and a repetition interval, T,over 4 GHz of bandwidth, the step size, Δf, is 15.625 MHz (e.g., 4 GHzof bandwidth divided by 256=15.625 MHz). FIG. 15B depicts a graph ofstepped frequency pulses that have a repetition interval, T, and a stepsize, Δf, of 62.5 MHz (e.g., 4 GHz of bandwidth divided by 64=62.5 MHz).As described above, an example sensor system has four RX antennas.Assuming a discrete frequency can be received on each RX antenna,degrees of freedom (DOF) of the sensor system in the receive operationscan be expressed as: 4 RX antennas×64 discrete frequencies=256 DOF; and4 RX antennas×256 discrete frequencies=1K DOF. The number of degrees offreedom (also referred to as “transmission frequency diversity”) canprovide signal diversity, which can be beneficial in an environment suchas the anatomy of a person. For example, the different discretefrequencies may have different responses to the different anatomicalfeatures of the person. Thus, greater transmission frequency diversitycan translate to greater signal diversity, and ultimately to moreaccurate health monitoring.

One feature of a stepped frequency transmission approach is that thesensor system receives reflected electromagnetic energy at basically thesame frequency over the repetition interval, T. That is, as opposed tochirp transmission, the frequency of the pulse does not change over theinterval of the pulse and therefore the received reflectedelectromagnetic energy is at the same frequency as the transmittedelectromagnetic energy for the respective interval. FIG. 16A depicts afrequency versus time graph of transmission pulses, with transmit (TX)interval and receive (RX) intervals identified relative to the pulses.As illustrated in FIG. 16A, RX operations for the first pulse occurduring the pulse length, τ, of repetition interval, T, and during theinterval between the next pulse. FIG. 16B depicts an amplitude versustime graph of the transmission waveforms that corresponds to FIG. 16A.As illustrated in FIG. 16B, the amplitude of the pulses is constantwhile the frequency increases by Δf at each repetition interval, T.

In an embodiment, the power of the transmitted electromagnetic energycan be set to achieve a desired penetration depth and/or a desiredillumination volume. In an embodiment, the transmission power from theTX antennas is about 15 dBm.

In an embodiment, electromagnetic energy can be transmitted from the TXantennas one TX antenna at a time (referred to herein as “transmitdiversity”). For example, a signal is transmitted from a first one ofthe two TX antennas while the second one of the two TX antennas is idleand then a signal is transmitted from the second TX antenna while thefirst TX antenna is idle. Transmit diversity may reveal thatillumination from one of the two TX antennas provides a higher qualitysignal than illumination from the other of the two TX antennas. This maybe especially true when trying to illuminate a vein whose location mayvary from person to person and/or from moment to moment (e.g., dependingon the position of the wearable device relative to the vein). Thus,transmit diversity can provide sets of received signals that areindependent of each other and may have different characteristics, e.g.,signal power, SNR, etc.

Some theory related to operating the sensor system using a steppedfrequency approach is described with reference to FIG. 17 , whichillustrates operations related to transmitting, receiving, andprocessing phases of the sensor system operation. With reference to theupper portion of FIG. 17 , a time versus amplitude graph of atransmitted signal burst, similar to the graph of FIG. 16B, is shown.The graph represents the waveforms of five pulses of a burst atfrequencies of f₀, f₀+Δf, f₀+2Δf, f₀+3Δf, and f₀+4Δf.

The middle portion of FIG. 17 represents values of received signals thatcorrespond to the amplitude, phase, and frequency of each pulse in theburst of four pulses. In an embodiment, received signals are placed inrange bins such that there is one complex sample per range bin perfrequency. Inverse Discrete Fourier Transforms (IDFTs) are thenperformed on a per-range bin basis to determine range information. Thebottom portion of FIG. 17 illustrates an IDFT process that produces asignal that corresponds to the range of a particular object. Forexample, the range may correspond to a vein such as the basilic vein. Inan embodiment, some portion of the signal processing is performeddigitally by a DSP or CPU. Although one example of a signal processingscheme is described with reference to FIG. 17 , other signal processingschemes may be implemented to isolate signals that correspond toreflections from blood in a vein (such as the basilic vein) from signalsthat correspond to reflections from other undesired anatomical features(such as tissue and bones) and from signals that correspond to leakagefrom the TX antennas.

Beamforming is a signal processing technique used in sensor arrays fordirectional signal transmission and/or reception. Beamforming can beimplemented by combining elements in a phased antenna array in such away that signals at particular angles experience constructiveinterference while other signals experience destructive interference.Beamforming can be used in both transmit operations and receiveoperations in order to achieve spatial selectivity, e.g., to isolatesome received signals from other received signals. In an embodiment,beamforming techniques are utilized to isolate signals that correspondto reflections from blood in a vein (such as the basilic vein) fromsignals that correspond to reflections from other undesired anatomicalfeatures (such as tissue and bones) and from signals that correspond toleakage from the TX antennas. An example of the concept of beamformingas applied to blood glucose monitoring using a wearable device such as asmartwatch is illustrated in FIG. 18 . In particular, FIG. 18 depicts anexpanded view of the anatomy of a wrist, similar to that described abovewith reference to FIGS. 2A-4D, relative to RX antennas 1846 of a sensorsystem 1810 that is integrated into a wearable device such as asmartwatch 1800. The anatomical features of the wrist that areillustrated in FIG. 18 include the skin 1822, a vein such as the basilicvein 1816, the radius bone 1834, and the ulna bone 1836. FIG. 18 alsoillustrates 2D representations of reception beams 1850 (although itshould be understood that the beams occupy a 3D space/volume) thatcorrespond to electromagnetic energy that is reflected from the blood inthe basilic vein to the respective RX antenna.

In an embodiment, a beamforming technique involves near-fieldbeamforming, where each RX antenna of the phased antenna array issteered independently to a different angle as opposed to far-fieldbeamforming where all of the antennas in a phased antenna array aresteered collectively to the same angle. For example, near-fieldbeamforming is used when the target is less than about 4-10 wavelengthsfrom the phased antenna array. In the case of a sensor system operatingat 122-126 GHz, 4-10 wavelengths is approximately within about 10-25 mmfrom the phased antenna array. In the case of monitoring a healthparameter related to blood, the blood vessels that are monitored (e.g.,the basilic and/or cephalic veins) are likely to be less than 10-25 mmfrom the phase antenna array. Thus, in an embodiment, near-fieldbeamforming techniques are used to isolate desired signals (e.g.,signals that correspond to reflections from blood in a vein such as thebasilic vein) from undesired signals (e.g., signals that correspond toreflections from other undesired anatomical features, such as tissue andbones, and from signals that correspond to leakage from the TXantennas). Beamforming can be accomplished in digital, in analog, or ina combination of digital and analog signal processing. In an embodiment,the ranging technique described above, which utilizes steppedfrequencies, is used in combination with beamforming to isolate signalsthat correspond to the reflection of electromagnetic energy from thebasilic vein.

The Doppler effect relates to the change in frequency or wavelength of awave (e.g., an electromagnetic wave) in relation to an observer, whichis moving relative to the source of the wave. The Doppler effect can beused to identify fluid flow by sensing the shift in wavelength ofreflections from particles moving with the fluid flow. In accordancewith an embodiment of the invention, signal processing based on theDoppler effect is applied to signals received by the sensor system toisolate signals that correspond to reflections from flowing blood fromsignals that correspond to reflections from objects that are stationary,at least with respect to the flowing blood. As described above,millimeter wave radio waves are transmitted below the skin to illuminateanatomical features below the skin. In the area of the body around thewrist, blood flowing through veins such as the basilic and cephalicveins is moving relative to the other anatomical features in the area.Thus, Doppler effect theory and corresponding signal processing is usedto filter for those signals that correspond to movement (movementrelative to other signals that correspond to stationary objects). In thehealth monitoring application as described herein, the signals thatcorrespond to the flowing blood can be identified by applying theDoppler effect theory to the signal processing to isolate the signalsthat correspond to the flowing blood. The isolated signals can then beused to measure a health parameter such as blood glucose level.

FIG. 19 illustrates an IC device 1922 similar to the IC device 822 shownin FIG. 8A relative to a vein 1916 such as the basilic or cephalic veinin the wrist area of a person. FIG. 19 also illustrates the flow ofblood through the vein relative to the IC device. Because the blood ismoving relative to the TX and RX antennas 1944 and 1946 of the sensorsystem, Doppler effect theory can be applied to signal processing of thereceived signals to isolate the signals that correspond to the flowingblood from the signals that correspond to objects that are stationaryrelative to the flowing blood. For example, received signals thatcorrespond to flowing blood are isolated from received signals thatcorrespond to stationary objects such as bone and fibrous tissue such asmuscle and tendons. In an embodiment, Doppler processing involvesperforming a fast Fourier transform (FFT) on samples to separate thesamples into component Doppler shift frequency bins. Frequency bins thatrepresent no frequency shift can be ignored (as they correspond toreflections from stationary objects) and frequency bins that represent afrequency shift (which corresponds to reflections from a moving object)can be used to determine a health parameter. That is, Doppler effectprocessing can be used to isolate signals that represent no frequencyshift (as they correspond to reflections from stationary objects) fromfrequency bins that represent a frequency shift (which correspond toreflections from a moving object). In an embodiment, Doppler effectsignal processing may involve sampling over a relatively long period oftime to achieve small enough velocity bins to decipher relativemovement. Thus, Doppler effect theory and corresponding signalprocessing can be used to filter for only those signals that correspondto movement (movement relative to the other received signals). Such anapproach allows signals that correspond to reflections from flowingblood, e.g., blood in a vein, to be isolated from other signals, e.g.,signals that correspond to stationary object. In an embodiment, Dopplersignal processing is performed digitally by a DSP and/or by a CPU.

With reference to FIG. 8A, during operation of the IC device 822, someelectromagnetic energy that is emitted from the TX antennas 844 will bereceived directly by at least one of the RX antennas 846 without firstpassing through the skin of the person. Signals that correspond to suchelectromagnetic energy do not correspond to a health parameter that isto be monitored and are referred to herein as electromagnetic energyleakage or simply as “leakage.” In an embodiment, various signalprocessing techniques may be implemented to mitigate the effects ofleakage. For example, signals that correspond to leakage should beisolated from signals that correspond to reflections of radio waves fromblood in a vein. In an embodiment, leakage is mitigated by applyingsignal processing to implement beamforming, Doppler effect processing,range discrimination or a combination thereof. Other techniques such asantenna design and antenna location can also be used to mitigate theeffects of leakage.

In an embodiment, signal processing to isolate signals that correspondto reflections of radio waves from blood in a vein from signals thatcorrespond to reflections of radio waves from other anatomical objects(such as bone and fibrous tissue such as muscle and tendons) and fromsignals that correspond to leakage can be implemented in part or in fulldigitally by a DSP. FIG. 20 is an embodiment of a DSP 2064 that includesa Doppler effect component 2070, a beamforming component 2072, and aranging component 2074. In an embodiment, the Doppler effect componentis configured to implement digital Doppler effect processing, thebeamforming component is configured to implement digital beamforming,and the ranging component is configured to implement digital ranging.Although the DSP is shown as including the three components, the DSP mayinclude fewer components and the DSP may include other digital signalprocessing capability. The DSP may include hardware, software, and/orfirmware or a combination thereof that is configured to implement thedigital signal processing that is described herein. In an embodiment,the DSP may be embodied as an ARM processor (Advanced RISC (reducedinstruction set computing) Machine). In some embodiments, components ofa DSP can be implemented in the same IC device as the RF front-end andthe TX and RX antennas. In other embodiments, components of the DSP areimplemented in a separate IC device or IC devices.

In an embodiment, the transmission of millimeter radio waves and theprocessing of signals that correspond to received radio waves is adynamic process that operates to locate signals corresponding to thedesired anatomy (e.g., signals that correspond to reflections of radiowaves from a vein) and to improve the quality of the desired signals(e.g., to improve the SNR). For example, the process is dynamic in thesense that the process is an iterative and ongoing process as thelocation of the sensor system relative to a vein or veins changes.

Although the techniques described above are focused on monitoring theblood glucose level in a person, the disclosed techniques are alsoapplicable to monitoring other parameters of a person's health such as,for example, blood pressure and heart rate. For example, thereflectively of blood in a vessel such as the basilic vein will changerelative to a change in blood pressure. The change in reflectivity asmonitored by the sensor system can be correlated to a change in bloodpressure and ultimately to an absolute value of a person's bloodpressure. Additionally, monitored changes in blood pressure can becorrelated to heart beats and converted over time to a heart rate, e.g.,in beats per minute. In other embodiments, the disclosed techniques canbe used to monitor other parameters of a person's health that areaffected by the chemistry of the blood. For example, the disclosedtechniques may be able to detect changes in blood chemistry thatcorrespond to the presence of foreign chemicals such as alcohol,narcotics, cannabis, etc. The above-described techniques may also beable to monitor other parameters related to a person, such as biometricparameters.

In an embodiment, health monitoring using the techniques describedabove, may involve a calibration process. For example, a calibrationprocess may be used for a particular person and a particular monitoringdevice to enable desired monitoring quality.

The above-described techniques are used to monitor a health parameter(or parameters) related to blood in a blood vessel or in blood vesselsof a person. The blood vessels may include, for example, arteries,veins, and/or capillaries. The health monitoring technique can targetblood vessels other than the basilic and/or cephalic veins. For example,other near-surface blood vessels (e.g., blood vessels in thesubcutaneous layer) such as arteries may be targeted. Additionally,locations other than the wrist area can be targeted for healthmonitoring. For example, locations in around the ear may be a desirablelocation for health monitoring, including, for example, the superficialtemporal vein and/or artery and/or the anterior auricular vein orartery. In an embodiment, the sensor system may be integrated into adevice such as a hearing aid or other wearable device that is attachedto the ear or around or near the ear. In another embodiment, locationsin and around the elbow joint of the arm may be a desirable location forhealth monitoring. For example, in or around the basilica vein or thecephalic vein at or near the elbow.

Although the techniques are described as using a frequency range of122-126 GHz, some or all of the above-described techniques may beapplicable to frequency ranges other than 122-126 GHz. For example, thetechniques may be applicable to frequency ranges around 60 GHz. Inanother embodiment, the techniques described herein may be applicable tothe 2-6 GHz frequency range. For example, a system similar to thatdescribed with reference to FIG. 6 may be used to implement healthmonitoring by transmitting and receiving RF energy in the 2-6 GHz range.In still another embodiment, multiple non-contiguous frequency rangesmay be used to implement health monitoring. For example, healthmonitoring may be implemented using both the 2-6 GHz frequency range andthe 122-126 GHz frequency range. For example, in an embodiment, steppedfrequency scanning in implemented in the lower frequency range and thenin the higher frequency range, or vice versa. Using multiplenon-contiguous frequency ranges (e.g., both the 2-6 GHz frequency rangeand the 122-126 GHz frequency range) may provide improved accuracy ofhealth monitoring.

In an embodiment, the sensor system may be embedded into a differentlocation in a monitoring device. For example, in an embodiment, a sensorsystem (or a portion of the sensor system such as IC device as shown inFIG. 8A) is embedded into an attachment device such as the strap of asmartwatch so that the sensor system can target a different blood vesselin the person. For example, the sensor system may be embedded into thestrap of a smartwatch so that a blood vessel at the side area of thewrist and/or at the anterior area of the wrist can be monitored. In suchan embodiment, the strap may include conductive signal paths thatcommunicate signals between the sensor IC device and the processor ofthe smartwatch.

FIG. 21 is a process flow diagram of a method for monitoring a healthparameter in a person. At block 2102, millimeter range radio waves aretransmitted over a three-dimensional (3D) space below the skin surfaceof a person. At block 2104, radio waves are received on multiple receiveantennas, the received radio waves including a reflected portion of thetransmitted radio waves. At block 2106, a signal is isolated from aparticular location in the 3D space in response to receiving the radiowaves on the multiple receive antennas. At block 2108, a signal thatcorresponds to a health parameter in the person is output in response tothe isolated signal. In an embodiment, the health parameter is bloodglucose level. In other embodiments, the health parameter may be bloodpressure or heart rate.

In an embodiment, health monitoring information that is gathered usingthe above-described techniques can be shared. For example, the healthmonitoring information can be displayed on a display device and/ortransmitted to another computing system via, for example, a wirelesslink.

As mentioned above, locations in around the ear may be desirable forhealth monitoring, including, for example, the superficial temporalartery or vein, the anterior auricular artery or vein, and/or theposterior auricular artery. FIG. 22A depicts a side view of the areaaround a person's ear 2200 with the typical approximate locations ofveins and arteries, including the superficial temporal artery 2202, thesuperficial temporal vein 2204, the anterior auricular artery 2206 andvein 2208, the posterior auricular artery 2210, the occipital artery2212, the external carotid artery 2214, and the external jugular vein2216. In an embodiment, a sensor system, such as the sensor systemdescribed herein, may be integrated into a device such as a hearing aidor another wearable device that is attached to the ear or around or nearthe ear. FIG. 22B depicts an embodiment of system 2250 in which at leastelements of an RF front-end 2222 (including the transmit and receiveantennas and corresponding transmit and receive components as shown inFIGS. 5-7 ) are located separate from a housing 2252 that includes, forexample, a digital processor, wireless communications capability, and asource of electric power, all of which are enclosed within the housing.For example, components of the digital baseband system as shown in FIG.5 may be enclosed within the housing and the housing is connected to theRF front-end by a communications medium 2254, such as a conductive wireor wires. In an embodiment, the housing 2252 is worn behind the ear 2200similar to a conventional hearing aid and the RF front-end 2222 islocated near a blood vessel that is around the ear. For example, the RFfront-end may include adhesive material that enables the RF front-end tobe adhered to the skin near a blood vessel such as, for example, thesuperficial temporal artery 2202 or vein 2204, the anterior auricularartery 2206 or vein 2208, and/or the posterior auricular artery 2210.FIG. 22C illustrates how a device, such as the device depicted in FIG.22B, may be worn near the ear 2200 of a person similar to how aconventional hearing aid is worn. FIG. 22C also shows the RF front-end2222 relative to the superficial temporal artery 2202 and thesuperficial temporal vein 2204 as shown in FIG. 22C. In an embodiment,the sensor system may be integrated with a conventional hearing aid toprovide both hearing assistance and health monitoring. For example, theintegrated system may include a housing, a speaker that is inserted intothe ear, and an RF front-end that is attached to the skin around the earand near to a blood vessel. In other embodiments, a sensor system may beintegrated into ear buds or into some other type of device that is wornaround or near the ear.

Although the magnitude of the reflected RF energy (also referred to asamplitude) that is received by the sensor system has been found tocorrespond to a health parameter, such as blood glucose level, it hasfurther been found that the combination of the amplitude and the phaseof the reflected RF energy can provide improved correspondence to ahealth parameter, such as a blood glucose level. Thus, in an embodiment,a value that corresponds to a health parameter of a person is generatedin response to amplitude and phase data that is generated in response toreceived radio waves. For example, the value that corresponds to ahealth parameter may be a value that indicates a blood glucose level inmg/dL or some other indication of the blood glucose level, a value thatindicates a person's heart rate (e.g., in beats per minute), and/or avalue that indicates a person's blood pressure (e.g., in millimeters ofmercury, mmHg). In an embodiment, a method for monitoring a healthparameter (e.g., blood glucose level) in a person involves transmittingradio waves below the skin surface of a person and across a range ofstepped frequencies, receiving radio waves on a two-dimensional array ofreceive antennas, the received radio waves including a reflected portionof the transmitted radio waves across the range of stepped frequencies,generating data that corresponds to the received radio waves, whereinthe data includes amplitude and phase data across the range of steppedfrequencies, and determining a value that is indicative of a healthparameter in the person in response to the amplitude and phase data. Inan embodiment, the phase data corresponds to detected shifts in sinewaves that are received at the sensor system. In another embodiment, avalue that is indicative of a health parameter in the person may bedetermined in response to phase data but not in response to amplitudedata.

Additionally, it has been found that certain step sizes in steppedfrequency scanning can provide good correspondence in health parametermonitoring. In an embodiment, the frequency range that is scanned usingstepped frequency scanning is on the order of 100 MHz in the 122-126 GHzrange and the step size is in the range of 100 kHz-1 MHz. For example,in an embodiment, the step size over the scanning range is around 100kHz (±10%).

Although the amplitude and phase of the reflected RF energy that isreceived by the sensor system has been found to correspond to a healthparameter, such as blood glucose level, it has further been found thatthe combination of the amplitude and phase of the reflected RF energyand some derived data, which is derived from the amplitude and/or phasedata, can provide improved correspondence to a health parameter, such asblood glucose level. Thus, in an embodiment, some data is derived fromthe amplitude and/or phase data that is generated by the sensor systemin response to the received RF energy and the derived data is used,often in conjunction with the amplitude and/or phase data, to determinea value that corresponds to a health parameter (e.g., the blood glucoselevel) of a person. For example, the data derived from the amplitudeand/or phase data may include statistical data such as the standarddeviation of the amplitude over a time window and/or the standarddeviation of the phase over a time window. In an embodiment, data can bederived from the raw data on a per-receive antenna basis or aggregatedamongst the set of receive antennas. In a particular example, it hasbeen found that the amplitude, phase, and the standard deviation ofamplitude over a time window (e.g., a time window of 1 second)corresponds well to blood glucose levels.

In an embodiment, a method for monitoring a health parameter (e.g.,blood glucose level) in a person involves transmitting radio waves belowthe skin surface of the person and across a range of steppedfrequencies, receiving radio waves on a two-dimensional array of receiveantennas, the received radio waves including a reflected portion of thetransmitted radio waves across the range of stepped frequencies,generating data that corresponds to the received radio waves, whereinthe data includes amplitude and phase data, deriving data from at leastone of the amplitude and phase data, and determining a value that isindicative of a health parameter in the person in response to thederived data. In an embodiment, the value is determined in response tonot only the derived data but also in response to the amplitude data andthe phase data. In an embodiment, the derived data is a statistic thatis derived from amplitude and/or phase data that is generated over atime window. For example, the statistic is one of a standard deviation,a moving average, and a moving mean. In other embodiments, the deriveddata may include multiple statistics derived from the amplitude and/orphase data. In an embodiment, a value that is indicative of a healthparameter is determined in response to a rich set of parametersassociated with the stepped frequency scanning including the scanningfrequency, the detected amplitudes and phases of the received RF energy,data derived from the detected amplitudes and phases, the state of thetransmit components, and the state of the receive components.

Using a sensor system, such as the sensor system described above, thereare various parameters to be considered in the stepped frequencyscanning process. Some parameters are fixed during operation of thesensor system and other parameters may vary during operation of thesensor system. Of the parameters that may vary during operation of thesensor system, some may be controlled and others are simply detected.FIG. 23 is a table of parameters related to stepped frequency scanningin a system such as the above-described system. The table includes anidentification of various parameters and an indication of whether thecorresponding parameter is fixed during operation (e.g., fixed as aphysical condition of the sensor system) or variable during operationand if the parameter is variable, whether the parameter is controlled,or controllable, during operation or simply detected during operation.In the table of FIG. 23 , “Time” refers to an aspect of time such as anabsolute moment in time relative to some reference (or may refer to atime increment, e.g., Δt). In an embodiment, the time corresponds to allof the other parameters in the table. That is, the state or value of allof the other parameters in the table is the state or value at that timein the stepped frequency scanning operation. “TX/RX frequency” refers tothe transmit/receive frequency of the sensor system at the correspondingtime as described above with reference to, for example, FIG. 6 . The TX1and TX2 state refers to the state of the corresponding transmitter(e.g., whether or not the corresponding power amplifiers (PAs) are on oroff) at the corresponding time. In an embodiment, RF energy transmittedfrom the transmission antennas can be controlled byactivating/deactivating the corresponding PAs. The RX1 and RX2 staterefers to the state of the corresponding receive paths (e.g., whether ornot components of the corresponding receive paths are active orinactive, which may involve powering on/off components in the receivepath) at the corresponding time. In an embodiment, the receiving of RFenergy on the receive paths can be controlled by activating/deactivatingcomponents of the corresponding receive paths. The RX detected amplituderefers to the amplitude of the received signals at the correspondingreceive path and at the corresponding time and the RX detected phaserefers to the phase (or phase shift) of the received signals at thecorresponding receive path and at the corresponding time. The TX and RXantenna 2D position refers to information about the 2D position of theantennas in the sensor system (e.g., the positions of the antennasrelative to each other or the positions of the antennas relative to acommon location) and the antenna orientation refers to antennacharacteristics that may be specific to a particular polarizationorientation. For example, a first set of antennas may be configured forvertical polarization while a second set of antennas is configured forhorizontal polarization in order to achieve polarization diversity.Other antenna orientations and/or configurations are possible. Asindicated in the table, antenna position and antenna orientation arefixed during stepped frequency scanning.

FIG. 24 is a table of parameters similar to the table of FIG. 23 inwhich examples are associated with each parameter for a given step in astepped frequency scanning operation in order to give some context tothe table. As indicated in FIG. 24 , the time is “t1” (e.g., someabsolute time indication or a time increment) and the operatingfrequency is “X GHz,” e.g., in the range of 2-6 GHz or 122-126 GHz. Inthe example of FIG. 24 , TX1, RX1, and RX4 are active and TX2, RX2, andRX3 are inactive during this step in the stepped frequency scanningoperation (e.g., at time t1). The detected amplitudes of RX1 and RX4 areindicated as “ampl1” and “ampl4” and the detected phases of RX1 and RX4are indicated as “ph1” and “ph4.” The detected amplitudes and phases ofRX2 and RX3 are indicated as “n/a” since the receive paths are inactive.The positions of the transmit and receive antennas are indicated in thelower portion of the table and correspond to the configuration describedabove with reference to

FIGS. 8A-8D and the antenna orientations are evenly distributed amongstvertical and horizontal orientations so as to enable polarizationdiversity. FIG. 25 depicts an embodiment of the IC device 820 from FIG.8A in which the antenna polarization orientation is illustrated by theorientation of the transmit and receive antennas 844 and 846,respectively. In FIG. 25 , rectangles with the long edges orientedvertically represent a vertical polarization orientation (e.g., antennasTX1, RX1, and RX4) and rectangles with the long edges orientedhorizontally represent a horizontal polarization orientation (e.g.,antennas TX2, RX2, and RX3). FIG. 24 reflects the same polarizationorientations in which TX1 is configured to vertically polarize thetransmitted RF energy and RX1 and RX4 are configured to receivevertically polarized RF energy and TX2 is configured to horizontallypolarize the transmitted RF energy and RX2 and RX3 are configured toreceive horizontally polarized RF energy. Although FIG. 24 is providedas an example, the parameter states of the variable parameters areexpected to change during stepped frequency scanning and the fixedparameters may be different in different sensor system configurations.

In an embodiment, during a stepped frequency scanning operation, certaindata, referred to herein as “raw data,” is generated. For example, theraw data is generated as digital data that can be further processed by adigital data processor. FIG. 26 is a table of raw data (e.g., digitaldata) that is generated during stepped frequency scanning. The raw datadepicted in FIG. 26 includes variable parameters of time, TX/RXfrequency, RX1 amplitude/phase, RX2 amplitude/phase, RX3amplitude/phase, and RX4 amplitude/phase. In the example of FIG. 26 ,the raw data corresponds to a set of data, referred to as a raw datarecord, which corresponds to one step in the stepped frequency scanning.For example, the raw data record corresponds to a particular frequencypulse as shown and described above with reference to FIG. 17 . In anembodiment, a raw data record also includes some or all of theparameters identified in FIGS. 23 and 24 . For example, the raw datarecord may include other variable and/or fixed parameters thatcorrespond to the stepped frequency scanning operation. In anembodiment, multiple raw data records are accumulated and processed by adigital processor, which may include a DSP, an MCU, and/or a CPU asdescribed above, for example, with reference to FIG. 5 . Raw data (e.g.,in the form of raw data records) may be used for machine learning.

As described above, it has been found that the combination of theamplitude and phase of reflected RF energy and some derived data, whichis derived from amplitude and/or phase data (e.g., from the “raw data”),can provide improved correspondence to a health parameter, such as bloodglucose level. Thus, in an embodiment, some data is derived from theamplitude and/or phase data that is generated by the sensor system inresponse to the received RF energy and the derived data is used, oftenin conjunction with the amplitude and/or phase data, to determine avalue that corresponds to a health parameter (e.g., the blood glucoselevel) of a person. For example, the data is derived from the raw datarecords that include the data depicted in FIGS. 23, 24, and 26 . Forexample, raw data records are accumulated over time and statistical datais derived from the accumulated raw data records. The statistical data,typically along with at least some portion of the raw data, is then usedto determine a value of a health parameter of a person.

Although it has been found that derived data from the amplitude and/orphase data can provide improved correspondence to a health parameter,such as blood glucose level, the particular model that provides adesired level of correspondence (e.g., that meets a predeterminedaccuracy) may need to be learned in response to a specific set ofoperating conditions. Thus, in an embodiment, a learning process (e.g.,machine learning) is implemented to identify and train a model thatprovides an acceptable correspondence to a health parameter such asblood glucose level.

FIG. 27 illustrates a system 2700 and process for machine learning thatcan be used to identify and train a model that reflects correlationsbetween raw data, derived data, and control data. For example, themachine learning process may be used to identify certain statistics(e.g., standard deviation of amplitude and/or phase over time) that canbe used to improve the correspondence of determined values to actualhealth parameters (such as blood glucose levels) in a person. Themachine learning process can also be used to train a model with trainingdata so that the trained model can accurately and reliably determinevalues for health parameters such as blood glucose level, bloodpressure, and/or heart rate in monitoring devices that are deployed inthe field. With reference to FIG. 27 , the system 2700 includes a sensorsystem 2710, a machine learning engine 2760, a trained model database2762, and a control element 2764.

In an embodiment, the sensor system 2710 is similar to or the same asthe sensor system described above. For example, the sensor system isconfigured to implement stepped frequency scanning in the 2-6 GHz and/or122-126 GHz frequency range using two transmit antennas and four receiveantennas. The sensor system generates and outputs raw data to themachine learning engine that can be accumulated and used as describedbelow.

In an embodiment, the control element 2764 is configured to provide acontrol sample to the sensor system 2710. For example, the controlelement includes a sample material 2766 (e.g., a fluid) that has a knownblood glucose level that is subjected to the sensor system.Additionally, in an embodiment, the control element is configured toprovide control data to the machine learning engine that corresponds tothe sample material. For example, the control element may include asample material that has a known blood glucose level that changes as afunction of time and the change in blood glucose level as a function oftime (e.g., Z(t) mg/dL) is provided to the machine learning engine 2760in a manner in which the raw data from the sensor system 2710 and thecontrol data can be time matched (e.g., synchronized). In anotherembodiment, the control element 2764 includes a sample material thatincludes a static parameter, e.g., a static blood glucose level inmg/dL, and the static parameter is manually provided to the machinelearning engine 2760 as the control data. For example, a particularsample is provided within range of RF energy 2770 that is transmittedfrom the sensor system (e.g., within a few millimeters), theconcentration of the sample is provided to the machine learning engine(e.g., manually entered), and the sensor system accumulates digital datathat corresponds to the received RF energy (including a reflectedportion of the transmitted RF energy) and that is correlated to thesample. In one embodiment, the sample material is provided in acontainer such as a vial and in another embodiment, the control elementincludes a person that is simultaneously being monitored by the sensorsystem (e.g., for the purposes of machine learning) and by a second,trusted, control monitoring system. For example, the control elementincludes a person who's blood glucose level, blood pressure, and/orheart rate is being monitored by a known (e.g., clinically accepted)blood glucose level, blood pressure, and/or heart rate monitor while theperson is simultaneously being monitored by the sensor system. The bloodglucose level, blood pressure, and/or heart rate information from theknown blood glucose level, blood pressure, and/or heart rate monitor isprovided to the machine learning engine as control data.

In an embodiment, the machine learning engine 2760 is configured toprocess the raw data received from the sensor system 2710, e.g., as rawdata records, and the control data received from the control element2764 to learn a correlation, or correlations, that provides acceptablecorrespondence to a health parameter such as blood glucose levels. Forexample, the machine learning engine is configured to receive raw datafrom the sensor system, to derive data from the raw data such asstatistical data, and to compare the derived data (and likely at leastsome portion of the corresponding raw data) to the control data to learna correlation, or correlations, that provides acceptable correspondencebetween a determined value of a health parameter and a controlled, orknown value, of the health parameter. In an embodiment, the machinelearning engine is configured to derive statistics from the raw datasuch as a standard deviation, a moving average, and a moving mean. Forexample, the machine learning engine may derive the standard deviationof the amplitude and/or phase of the received RF energy and thencorrelate the derived statistic(s) and the raw data to the control datato find a correlation that provides an acceptable correspondence betweenthe raw data, the derived data, and the actual value of the healthparameter as provided in the control data. In an embodiment,correspondence between the raw data, the derived data, and the actualvalues of the health parameter in a control sample is expressed in termsof a correspondence threshold, which is indicative of, for example, thecorrespondence between values of a health parameter generated inresponse to the raw data, the derived data, and actual values of thehealth parameter in a control sample. For example, a correspondence isexpressed as a percentage of correspondence to the actual value of thecontrol sample such that a generated concentration value of a bloodglucose level of 135 mg/dL and a value of a control sample at 140 mg/dLhas a correspondence of 135/140=96.4%. In an embodiment, acorrespondence threshold can be set to accept only those correlationsthat produce correspondence that meets a desired correspondencethreshold. In an embodiment, a correspondence threshold of a generatedvalue to the value of a control sample of within ±10% of the controlsample is acceptable correspondence. In another embodiment, acorrespondence threshold of within ±10% of the control sample in 95% ofthe measurements is acceptable correspondence.

FIG. 28 is an example of a process flow diagram of a method forimplementing machine learning using, for example, the system describedabove with reference to FIG. 27 to select a correlation (e.g., a modelor algorithm) that provides acceptable correspondence between values ofa health parameter generated in response to the raw data, the deriveddata, and actual values of the health parameter in the control samples.At block 2802, raw data is obtained from the sensor system. At block2804, the raw data is correlated to known control data, such as knownblood glucose levels. At decision point 2806, it is determined whether acorrelation between the raw data and the control data is acceptable,e.g., whether the correspondence is within an acceptable threshold. Ifit is determined that there is an acceptable correspondence, then theprocess proceeds to block 2808, where the correlation (e.g., a model oralgorithm) is saved and then the initial learning process is ended. Ifat decision point 2806 it is determined that there is not an acceptablecorrespondence between the raw data and the control data (e.g., thecorrespondence is not within an acceptable threshold), then the processproceeds to block 2810. At block 2810, additional data is derived fromthe raw data. For example, the machine learning engine may derive astatistic or statistics from the raw data such as a standard deviation,a moving average, and a moving mean. For example, the machine learningengine may derive the standard deviation of the amplitude and/or phaseof the received RF energy. At decision point 2812, it is determinedwhether a correlation between the raw data, the derived data, and thecontrol data is acceptable (e.g., the correspondence is within anacceptable threshold). If it is determined that there is an acceptablecorrespondence between the raw data, the derived data, and the controldata, then the process proceeds to block 2814, where the correlation(e.g., a model or algorithm) is saved and then the initial learningprocess is ended. If at decision point 2812 it is determined that thereis not an acceptable correspondence between the raw data, the deriveddata, and the control data (e.g., the correspondence is not within anacceptable threshold), then the process returns to block 2810. At block2810, additional data is derived from the raw data and/or from thederived data. For example, a different statistic, or statistics, isderived from the raw data and/or from the previously derived data. In anembodiment, the exploration of correlations between the raw data, thederived data, and the control data is an iterative process thatconverges on a correlation, or correlations, which provides acceptablecorrespondence between the raw data, the derived data, and the controldata. In an embodiment, the machine learning process can be repeatedlyused to continue to search for correlations that may improve thecorrespondence between the raw data, the derived data, and the controldata to improve the accuracy of health parameter monitoring.

In an embodiment, the above-described process is used for algorithmselection and/or model building as is done in the field of machinelearning. In an embodiment, algorithm selection and/or model buildinginvolves supervised learning to recognize patterns in the data (e.g.,the raw data, the derived data, and/or the control data). In anembodiment, the algorithm selection process may involve utilizingregularized regression algorithms (e.g., Lasso Regression, RidgeRegression, Elastic-Net), decision tree algorithms, and/or treeensembles (random forests, boosted trees).

In an embodiment, acceptable correlations that are learned by themachine learning engine are trained by the machine learning engine toproduce a trained model, or trained models, that can be deployed in thefield to monitor a health parameter of a person. Referring back to FIG.27 , a model that is trained by the machine learning engine 2760 is heldin the trained model database 2762. In an embodiment, the trained modeldatabase may store multiple models that have been found to provideacceptable correspondence between generated values of a health parameterand the actual values of the health parameter as provided in the controldata. Additionally, the trained model database 2762 may provide rules onhow to apply the model in deployed sensor systems. For example,different models may apply to different deployment conditions, e.g.,depending on the location of the RF front-end relative to a bloodvessel, environmental conditions, etc.

In an embodiment, operation of the system 2700 shown in FIG. 27 togenerate training data and to train a model using the training datainvolves providing a control sample in the control element 2764 and thenoperating the sensor system 2700 to implement stepped frequency scanningover a desired frequency range that is within, for example, the 2-6 GHzand/or 122-126 GHz frequency range. For example, control datacorresponding to the control sample 2766 is provided to the machinelearning engine 2760 and raw data generated from the sensor system 2710is provided to the machine learning engine. The machine learning enginegenerates training data by combining the control data with the steppedfrequency scanning data in a time synchronous manner. The machinelearning engine processes the training data to train a model, or models,which provides an acceptable correspondence between generated values ofa health parameter and the control data. The model, or models, is storedin the trained model database 2762, which can then be applied to asystem 2700 that is deployed in the field to monitor a health parameterof a person. In an embodiment, the sensor system is exposed to multipledifferent samples under multiple different operating conditions togenerate a rich set of training data.

In an embodiment, the goal of the training process is to produce atrained model that provides a high level of accuracy and reliability inmonitoring a health parameter in a person over a wide set of parameterranges and operational and/or environmental conditions. For example, thecorrespondence of a model during training can be expressed in terms of acorrespondence threshold, which is indicative of, for example, thecorrespondence between values of a health parameter generated inresponse to the raw data, the derived data, and actual values of thehealth parameter in a control sample. For example, a correspondence isexpressed as a percentage of correspondence to the actual value of thecontrol sample such that a generated concentration value of a bloodglucose level of 135 mg/dL and a value of a control sample at 140 mg/dLhas a correspondence of 135/140=96.4%. In an embodiment, acorrespondence threshold can be set for a trained model so that thetrained model produces correspondence that meets a desiredcorrespondence threshold. In an embodiment, a correspondence thresholdof a generated value to the value of a control sample of within ±10% ofthe control sample is acceptable correspondence for a trained model. Inanother embodiment, a correspondence threshold of within ±10% of thecontrol sample in 95% of the measurements is acceptable correspondencefor a trained model.

In an embodiment, the correspondence between the raw and/or derived dataand the control data may change in response to different factorsincluding, for example, over different blood glucose levels, differentmonitoring locations, different environmental conditions, etc. Thus, insome embodiments, the trained model database 2762 may include multipledifferent trained models that are applicable to certain conditions.Additionally, the trained model database may evolve over time as moreinformation is gathered and/or as different correlations are discovered.

As described above, the model training process utilizes raw data (e.g.,in the form of raw data records) as inputs into the machine learningengine. FIG. 29 is an example of a table of a raw data record (e.g.,digital data) generated during stepped frequency scanning that is usedto generate the training data. The raw data record includes time t1, aknown blood glucose level (e.g., a control sample with a knownconcentration of glucose in mg/dL, Z mg/dL) at the time t1, TX/RXfrequency at the time t1, RX1 amplitude/phase, RX2 amplitude/phase, RX3amplitude/phase, and RX4 amplitude/phase at the time t1. In the exampleof FIG. 29 , the raw data record includes the glucose level of thecontrol sample at the same time the amplitude and phase of the RF energywas received by the sensor system, thus, the control data is combinedwith the stepped frequency scanning data in a time synchronous manner.In addition, the raw data records that are used to generate the trainingdata may include some or all of the parameters identified in FIGS. 23and 24 . For example, the raw data records and the correspondingtraining data may include other variable and/or fixed parameters thatcorrespond to the stepped frequency scanning operation to provide a richset of parameters from which to generate the training data.

In a stepped frequency scanning operation, multiple raw data records aregenerated as the sensor system scans across a frequency range. FIGS.30A-30D are tables of at least portions of raw data records that aregenerated during a learning process that spans the time of t1-tn, wheren corresponds to the number (e.g., an integer of 2 or greater) of timeintervals, T, in the stepped frequency scanning. Each of the raw datarecords includes control data (e.g., known glucose level, Z mg/dL) thatis combined with stepped frequency scanning data in a time synchronousmanner.

With reference to FIG. 30A, at time, t1, the raw data record includesthe time, t1, a known blood glucose level (e.g., Z1 in mg/dL) at timet1, a TX/RX frequency (e.g., X GHz) at time t1, RX1 amplitude/phase attime t1 (ampl1-t1/ph1-t1), RX2 amplitude/phase at time t1(ampl2-t1/ph2-t1), RX3 amplitude/phase at time t1 (ampl3-t1/ph3-t1), andRX4 amplitude/phase at time t1 (ampl4-t1/ph4-t1). In the steppedfrequency scanning, at the next time, t2, the frequency is changed byone step size, e.g., incremented by Δf. In an embodiment, the steppedfrequency scanning operation generates 200 raw data records per second,e.g., a sample rate of 200 samples/second. With reference to FIG. 30B,at time, t2, the raw data record includes the time, t2, a known bloodglucose level (e.g., Z2 in mg/dL) at time t2, a TX/RX frequency (e.g.,X+Δf GHz) at time t2, RX1 amplitude/phase at time t2 (ampl1-t2/ph1-t2),RX2 amplitude/phase at time t2 (ampl2-t2/ph2-t2), RX3 amplitude/phase attime t2 (ampl3-t2/ph3-t2), and RX4 amplitude/phase at time t2(ampl4-t2/ph4-t2). With reference to FIG. 30C, at time, t3, the raw datarecord includes the time, t3, a known blood glucose level (e.g., Z3 inmg/dL) at time t3, a TX/RX frequency (e.g., X+2Δf GHz) at time t3, RX1amplitude/phase at time t3 (ampl1-t3/ph1-t3), RX2 amplitude/phase attime t3 (ampl2-t3/ph2-t3), RX3 amplitude/phase at time t3(ampl3-t3/ph3-t3), and RX4 amplitude/phase at time t3 (ampl4-t3/ph4-t3).With reference to FIG. 30D, at time, tn, the raw data record includesthe time, tn, a known blood glucose level (e.g., Zn in mg/dL) at timetn, a TX/RX frequency (e.g., X+(n−1)Δf GHz) at time tn, RX1amplitude/phase at time tn (ampl1-tn/ph1-tn), RX2 amplitude/phase attime tn (ampl2-tn/ph2-tn), RX3 amplitude/phase at time tn(ampl3-tn/ph3-tn), and RX4 amplitude/phase (ampl4-tn/ph4-tn) at time tn.

As illustrated above, raw data is collected on a per-antenna basis forthe amplitude and/or phase of the received RF energy. Raw data collectedon a per-antenna basis for amplitude and phase for the example of FIGS.30A-30D may include:ampl1: ampl1-t1, ampl1-t2, ampl1-t3, . . . , ampl1-tn;ampl2: ampl2-t1, ampl2-t2, ampl2-t3, . . . , ampl2-tn;ampl3: ampl3-t1, ampl3-t2, ampl3-t3, . . . , ampl3-tn;ampl4; ampl4-t1, ampl4-t2, ampl4-t3, . . . , ampl4-tn;ph1: ph1-t1, ph1-t2, ph1-t3, . . . , ph1-tn;ph2: ph2-t1, ph2-t2, ph2-t3, . . . , ph2-tn;ph3: ph3-t1, ph3-t2, ph3-t3, . . . , ph3-tn); andph4: ph4-t1, ph4-t2, ph4-t3, . . . , ph4-tn).

In the example of FIGS. 30A-30D, the standard deviation may becalculated on a per-antenna basis for the amplitude and phase and is afunction of the following raw data elements:σ(ampl1)=f(ampl1-t 1+ampl1-t 2+ampl1-t 3+ . . . +ampl1-tn);σ(ampl2)=f(ampl2-t 1+ampl2-t 2+ampl2-t 3+ . . . +ampl2-tn);σ(ampl3)=f(ampl3-t 1+ampl3-t 2+ampl3-t 3+ . . . +ampl3-tn);σ(ampl4)=f(ampl4-t 1+ampl4-t 2+ampl4-t 3+ . . . +ampl4-tn);σ(ph 1)=f(ph 1-t 1+ph 1-t 2+ph 1-t 3+ . . . +ph 1-tn);σ(ph 2)=f(ph 2-t 1+ph 2-t 2+ph 2-t 3+ . . . +ph 2-tn);σ(ph 3)=f(ph 3-t 1+ph 3-t 2+ph 3-t 3+ . . . +ph 3-tn); andσ(ph 4)=f(ph 4-t 1+ph 4-t 2+ph 4-t 3+ . . . +ph 4-tn).

In an embodiment, data is derived on a per-antenna basis. In otherembodiments, data such as statistics can be derived from datacorresponding to different combinations of antennas.

Raw data records collected over time can be used as described above tolearn correlations (e.g., a model or algorithm) between the raw data,derived data, and the control data and to train a model. In anembodiment, a rich set of training data is collected and processed totrain a model that can provide accurate and reliable measurements of ahealth parameter such as blood glucose level, blood pressure, and/orheart rate. In an embodiment, the raw data including amplitude and phaseand the derived data including the standard deviation of the amplitudehas been found to correspond well to the health parameter of bloodglucose level.

Once correlations between the raw data, the derived data, and thecontrol data have been learned and a model has been trained, a sensorsystem can be deployed into the field for use in monitoring a healthparameter of a person, such as the blood glucose level. FIG. 31illustrates a system 3100 for health parameter monitoring that utilizesa sensor system similar to or the same as the sensor system describedabove. With reference to FIG. 31 , the system includes a sensor system3110, a health parameter determination engine 3180, and a trained modeldatabase 3182.

In an embodiment, the sensor system 3110 is similar to or the same asthe sensor system described above. For example, the sensor system isconfigured to implement stepped frequency scanning in the 2-6 GHz and/or122-126 GHz frequency range using two transmit antennas and four receiveantennas. The sensor system generates and outputs raw data to the healthparameter determination engine 3180 that can be accumulated and used togenerate and output a value that corresponds to a health parameter.

A model (or models) that is trained by the machine learning engine asdescribed above is held in the trained model database 3182. In anembodiment, the trained model database may store multiple models thathave been trained to provide acceptable correspondence between agenerated value of a health parameter and the actual value of the healthparameter as provided in the control data. Additionally, the trainedmodel database may provide rules on how to apply trained models indeployed sensor systems. In an embodiment, the trained model databaseincludes memory for storing a trained model, or models. The memory mayinclude, for example, RAM, SRAM, and/or SSD.

In an embodiment, the health parameter determination engine 3180 isconfigured to generate an output that corresponds to a health parameterin response to the raw data received from the sensor system 3110,derived data, and using a trained model that is stored in the trainedmodel database 3182. For example, the health parameter determinationengine 3180 outputs a value that indicates a blood glucose level inmg/dL or some other indication of the blood glucose level. In otherembodiments, the health parameter determination engine may output avalue that is an indication of a person's heart rate (e.g., in beats perminute) and/or an indication of a person's blood pressure (e.g., inmillimeters of mercury, mmHg). In other embodiments, the “values” outputby the health parameter determination engine may correspond to a healthparameter in other ways. For example, the output value may indicate avalue such as “high,” “medium,” “low” with respect to a health parameter(e.g., a high blood glucose level, a medium blood glucose level, or alow blood glucose level relative to a blood glucose scale), the outputvalue may indicate a color, such as green, yellow, or red that indicatesa health parameter, or the output value, may indicate a range of values,such as 130-140 mg/dL blood glucose, 70-80 beats per minute, or 110-120mmHg blood pressure. In an embodiment, the health parameterdetermination engine recognizes patterns in the raw and/or derived dataand applies the recognized patterns to the trained model to generate anoutput that corresponds to a health parameter in a person. The healthparameter determination engine may be implemented by a digitalprocessor, such as a CPU or MCU, in conjunction with computer readableinstructions that executed by the digital processor.

In an embodiment, operation of the system 3100 shown in FIG. 31 involvesbringing a portion of a person's anatomy 3186 (such as a wrist, arm, orear area) into close proximity to the sensor system 3110 (or bringingthe sensor system into close proximity to the portion of a person'sanatomy) and operating the sensor system to implement stepped frequencyscanning over a frequency range, e.g., in the range of 122-126 GHz suchthat transmitted RF energy 3170 penetrates below the surface of theperson's skin. Raw data generated from implementing the steppedfrequency scanning is output from the sensor system and received at thehealth parameter determination engine 3180. The health parameterdetermination engine processes the raw data in conjunction with at leastone trained model from the trained model database 3182 to generate avalue that corresponds to a health parameter of the person, e.g., avalue that corresponds to the blood glucose level of the person. In anembodiment, the value that corresponds to the health parameter isoutput, for example, as a graphical indication of the blood glucoselevel. In an embodiment, the generated value may be stored in a healthparameter database for subsequent access.

In an embodiment, the system 3100 depicted in FIG. 31 is implemented ina device such as a smartwatch or smartphone. In other embodiments, someportion of the system (e.g., the RF front-end) is implemented in adevice, such as a dongle, a patch, a smartphone case, or some otherdevice and the health parameter determination engine and the trainedmodel correlations database is implemented in a nearby device such as asmartphone. For example, in one embodiment, the sensor system isembodied in a device that attaches near the ear of a person and raw datais communicated via a wireless connection to a device such as asmartphone that processes the raw data to generate a value thatcorresponds to the blood glucose level of the person.

FIG. 32 is a process flow diagram of a method for monitoring a healthparameter in a person. At block 3202, radio waves are transmitted belowthe skin surface of a person and across a range of stepped frequencies.At block 3204, radio waves are received on a two-dimensional array ofreceive antennas, the received radio waves including a reflected portionof the transmitted radio waves across the range of stepped frequencies.At block 3206, data that corresponds to the received radio waves isgenerated, wherein the data includes amplitude and phase data. At block3208, a value that is indicative of a health parameter in the person isdetermined in response to the amplitude and phase data.

FIG. 33 is a process flow diagram of another method for monitoring ahealth parameter in a person. At block 3302, radio waves are transmittedbelow the skin surface of a person and across a range of steppedfrequencies. At block 3304, radio waves are received on atwo-dimensional array of receive antennas, the received radio wavesincluding a reflected portion of the transmitted radio waves across therange of stepped frequencies. At block 3306, data that corresponds tothe received radio waves is generated, wherein the data includesamplitude and phase data. At block 3308, data is derived from at leastone of the amplitude and phase data. At block 3310, a value that isindicative of a health parameter in the person is determined in responseto the derived data.

FIG. 34 is a process flow diagram of a method for training a model foruse in monitoring a health parameter in a person. At block 3402, controldata that corresponds to a control element is received, wherein thecontrol data corresponds to a health parameter of a person. At block3404, stepped frequency scanning data that corresponds to radio wavesthat have reflected from the control element is received, wherein thestepped frequency scanning data includes frequency and correspondingamplitude and phase data over a range of frequencies. At block 3406,training data is generated by combining the control data with thestepped frequency scanning data in a time synchronous manner. At block3408, a model is trained using the training data to produce a trainedmodel, wherein the trained model correlates stepped frequency scanningdata to values that are indicative of a health parameter of a person.

Although the operations of the method(s) herein are shown and describedin a particular order, the order of the operations of each method may bealtered so that certain operations may be performed in an inverse orderor so that certain operations may be performed, at least in part,concurrently with other operations. In another embodiment, instructionsor sub-operations of distinct operations may be implemented in anintermittent and/or alternating manner.

It should also be noted that at least some of the operations for themethods described herein may be implemented using software instructionsstored on a computer useable storage medium for execution by a computer.As an example, an embodiment of a computer program product includes acomputer useable storage medium to store a computer readable program.

The computer-useable or computer-readable storage medium can be anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system (or apparatus or device). Examples ofnon-transitory computer-useable and computer-readable storage mediainclude a semiconductor or solid state memory, magnetic tape, aremovable computer diskette, a random access memory (RAM), a read-onlymemory (ROM), a rigid magnetic disk, and an optical disk. Currentexamples of optical disks include a compact disk with read only memory(CD-ROM), a compact disk with read/write (CD-R/W), and a digital videodisk (DVD).

Alternatively, embodiments of the invention may be implemented entirelyin hardware or in an implementation containing both hardware andsoftware elements. In embodiments which use software, the software mayinclude but is not limited to firmware, resident software, microcode,etc.

Although specific embodiments of the invention have been described andillustrated, the invention is not to be limited to the specific forms orarrangements of parts so described and illustrated. The scope of theinvention is to be defined by the claims appended hereto and theirequivalents.

What is claimed is:
 1. A method for monitoring a health parameter in aperson, the method comprising: transmitting, from a wearable device,radio waves below the skin surface of a person and across a range ofstepped frequencies; receiving radio waves on each receive antenna of atwo-dimensional array of receive antennas and on correspondingantenna-specific receive paths of the wearable device in which eachantenna-specific receive path includes its own set of receivecomponents, the received radio waves including a reflected portion ofthe transmitted radio waves across the range of stepped frequencies;generating, on a per-receive antenna basis, antenna-specific data thatcorresponds to the radio waves received on each one of the receiveantennas of the two-dimensional array of receive antennas, wherein theantenna-specific data includes antenna-specific amplitude data andantenna-specific phase data; deriving, on a per-receive antenna basis,data from at least one of the antenna-specific amplitude data and theantenna-specific phase data to generate antenna-specific derived datathat corresponds to the radio waves received on each one of the receiveantennas of the two-dimensional array of receive antennas; anddetermining a value that is indicative of a health parameter in theperson in response to the antenna-specific derived data that correspondsto the radio waves received on all of the receive antennas of thetwo-dimensional array of receive antennas.
 2. The method of claim 1,wherein the value that is indicative of a health parameter in the personis determined in response to the antenna-specific amplitude data, theantenna-specific phase data, and the antenna-specific derived data. 3.The method of claim 1, wherein the value that is indicative of a healthparameter in the person is determined in response to theantenna-specific amplitude data, the antenna-specific phase data, andthe antenna-specific derived data, wherein the antenna-specific deriveddata comprises the standard deviation of the antenna-specific amplitudedata.
 4. The method of claim 1, wherein the antenna-specific deriveddata comprises a statistic that is derived from antenna-specificamplitude and/or antenna-specific phase data that is generated over atime window.
 5. The method of claim 4, wherein the statistic is one of astandard deviation, a moving average, and a moving mean.
 6. The methodof claim 1, wherein the antenna-specific derived data is a statisticthat is derived from antenna-specific amplitude data that is generatedover a time window.
 7. The method of claim 6, wherein the statistic isone of a standard deviation, a moving average, and a moving mean.
 8. Themethod of claim 1, wherein the antenna-specific derived data is astandard deviation of antenna-specific amplitude data that is generatedover a time window.
 9. The method of claim 1, wherein theantenna-specific derived data is a standard deviation ofantenna-specific phase data that is generated over a time window. 10.The method of claim 1, wherein the health parameter is blood glucoselevel.
 11. The method of claim 1, wherein the health parameter is bloodpressure.
 12. The method of claim 1, wherein the health parameter isheart rate.
 13. The method of claim 1, wherein radio waves aretransmitted from transmit antennas that have at least two differentpolarization orientations and wherein radio waves are received onantennas in the two-dimensional array of receive antennas that havepolarization orientations that correspond to the transmit antennas. 14.A method for monitoring a blood glucose level in a person, the methodcomprising: transmitting, from a wearable device, radio waves below theskin surface of a person and across a range of stepped frequencies;receiving radio waves on each receive antenna of a two-dimensional arrayof receive antennas and on corresponding antenna-specific receive pathsof the wearable device in which each antenna-specific receive pathincludes its own set of receive components, the received radio wavesincluding a reflected portion of the transmitted radio waves across therange of stepped frequencies; generating, on a per-receive antennabasis, antenna-specific data that correspond to radio waves received oneach one of the receive antennas of the two-dimensional array of receiveantennas, wherein the antenna-specific data includes antenna-specificamplitude data and antenna-specific phase data; deriving, on aper-receive antenna basis, a parameter from at least one of theantenna-specific amplitude data and the antenna-specific phase data togenerate an antenna-specific derived parameter that corresponds to eachone of the receive antennas of the two-dimensional array of receiveantennas; and outputting a value that is indicative of the blood glucoselevel in response to the antenna-specific derived parameters thatcorrespond to the radio waves received on all of the receive antennas ofthe two-dimensional array of receive antennas.
 15. The method of claim14, wherein that value that is indicative of the blood glucose level inthe person is determined in response to the antenna-specific amplitudedata, the antenna-specific phase data, and the antenna-specific derivedparameters.
 16. The method of claim 14, wherein the antenna-specificderived parameters comprise a statistic that is derived from theantenna-specific amplitude and the antenna-specific phase data that isgenerated over a time window.
 17. The method of claim 14, wherein theantenna-specific derived parameters comprise a statistic that is derivedfrom antenna-specific amplitude data that is generated over a timewindow.
 18. The method of claim 17, wherein the statistic is one of astandard deviation, a moving average, and a moving mean.
 19. The methodof claim 14, wherein the antenna-specific derived parameters comprise astatistic that is derived from antenna-specific phase data that isgenerated over a time window.
 20. The method of claim 19, wherein thestatistic is one of a standard deviation, a moving average, and a movingmean.
 21. The method of claim 14, wherein the antenna-specific derivedparameters comprise a standard deviation of the antenna-specificamplitude data that is generated over a time window.
 22. The method ofclaim 14, wherein the antenna-specific derived parameters comprise astandard deviation of the antenna-specific phase data that is generatedover a time window.
 23. The method of claim 14, wherein radio waves aretransmitted from transmit antennas that have at least two differentpolarization orientations and wherein radio waves are received onantennas in the two-dimensional array of receive antennas that havepolarization orientations that correspond to the transmit antennas.